Girl Geek X MosaicML Lightning Talks (Video + Transcript)

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Over 120 girl geeks joined networking and talks at the sold-out MosaicML Girl Geek Dinner from women working in machine learning at MosaicML, Meta AI, Atomwise, Salesforce Research, OpenAI, Amazon, and Hala Systems.

Speakers discuss efficient machine learning training with MosaicML, reinforcement learning, ML-based drug discovery with AtomNet, evaluating recommendation robustness with RGRecSys, turning generative models into products at OpenAI, seeking the bigger picture at AWS, and more.

Table of Contents

0 – Intro – Angie Chang, Founder at Girl Geek X – watch her talk or read her words

1 – Welcome – Julie Choi, VP and Chief of Growth at MosaicMLwatch her talk or read her words

2 – Making ML Training Faster, Algorithmically – Laura Florescu, AI Researcher at MosaicMLwatch her talk or read her words

3 – Reinforcement Learning: A Career Journey – Amy Zhang, Research Scientist at Meta AIwatch her talk or read her words

4 – Addressing Challenges in Drug Discovery – Tiffany Williams, Staff Software Engineer at Atomwisewatch her talk or read her words

5 – Evaluating Recommendation System Robustness – Shelby Heinecke, Senior Research Scientist at Salesforce Researchwatch her talk or read her words

6 – Turning Generative Models From Research Into Products – Angela Jiang, Product Manager at OpenAIwatch her talk or read her words

7 – Seeking the Bigger Picture – Banu Nagasundaram, Machine Learning Product Leader at Amazon Web Serviceswatch her talk or read her words

8 – 10 Lessons Learned from Building High Performance Diverse Teams – Lamya Alaoui, Director of People Ops at Hala Systemswatch her talk or read her words

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Transcript of MosaicML Girl Geek Dinner – Lightning Talks:

Angie Chang: Thank you so much for coming out. I’m so glad you’re here. My name is Angie Chang and I’m the founder of Girl Geek X. We’ve been doing Girl Geek Dinners in the San Francisco Bay Area for, if you can believe it, almost 15 years now. It’s the first Girl Geek Dinner in over two years, because Julie is a rock star and wanted to do a Girl Geek Dinner in person in the pandemic and we’re like, “Yes!” It was postponed and now in May, we’re finally doing this event. I’m so glad that we have a sold out event of amazing women in machine learning that we’re going to be hearing from tonight!

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Girl Geek X founder Angie Chang welcomes the sold-out crowd to our first IRL Girl Geek Dinner in over two years during the pandemic! (Watch on YouTube)

Angie Chang: I don’t want to steal too much of the time, but I wanted to do a quick raffle of a tote bag that I have. I’m going to ask, who has been to the first Girl Geek Dinner and can name a speaker from that event? Sometimes I meet people who have. Are we making this really hard? Okay, the first Girl Geek Dinner was at Google. We had over 400 women show up for a panel of inspiring women. I just wanted to see because that’s a lot of people. Who thinks they’ve been to the most Girl Geek Dinners in the room? Okay.

Audience Member: It’s actually not me, but like, this clutch was designed by somebody who hates women because its super heavy – and I see that that [Girl Geek X tote] has handles.

Angie Chang: Okay, so we have a full agenda of machine learning lightning talks and I’m going to introduce you to our host for tonight. Julie Choi is the Chief Growth Officer of MosaicML and she is an amazing supporter of women and I would like to invite her up.

Julie Choi: Oh, thank you. Hi everyone. Thank you so much for coming out to the MosaicML Girl Geek Dinner. I am Julie Choi and I actually did go to the first Girl Geek Dinner. I want to thank Angie Chang and the Girl Geek Dinner organization. Angie has just been a pioneer and truly, just a very special person, bringing us together ever since, was that 2010 or something, I don’t know. When was that?

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MosaicML VP and Chief Growth Officer Julie Choi welcomes the audience. She emcees the evening at MosaicML Girl Geek Dinner. (Watch on YouTube)

Angie Chang: The first event was 2008.

Julie Choi: 2008. Yes. And tonight we have amazing speakers to share with us about machine learning, about engineering, about diversity, and how that can really supercharge productivity in high growth organizations and machine learning research just from some of the world’s best AI companies and organizations.

Julie Choi: Our first speaker of the evening is my dear colleague at MosaicML, Laura Florescu. Laura and I met a little over a year ago. You greeted me at the front door. You were the first face I saw at MosaicML. She has just been an inspiration to me as an ML researcher at our company. Prior to joining MosaicML, Laura actually worked at several unicorn AI hardware startups. Then prior to that, she got her PhD from NYU in mathematics and is just a brilliant lady. Laura, can you come up and tell us about this amazing topic?

Laura Florescu: Thank you.

Julie Choi: Let’s give her a hand.

Laura Florescu: Can you hear me? Hi, thank you so much everybody for being here. Thanks to Julie, Sarah, Angie, Playground, for having this event. Very honored to be here.

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MosaicML AI Researcher Laura Florescu talks about making ML training faster, algorithmically with Composer and Compute, MosaicML’s latest offerings for efficient ML. (Watch on YouTube)

Laura Florescu: As Julie said, I’m a researcher at MosaicML. A little bit about myself. I’m originally from Romania. I came to the states, did my undergrad in math, did a PhD at NYU, and then I got the kind of Silicon Valley bug. And now I’m at MosaicML. And so what we do is develop algorithms and infrastructure to train neural networks efficiently.

Laura Florescu: Basically for the people in the audience who are not into ML, training neural networks is at the core of artificial intelligence. It uses a lot of data. It’s applied to many different fields with image, language, speech, and kind of like a takeaway from this is that, for large powerful models, the training costs for one single run can get in the millions of dollars, to train one such model. And in order to get to a really good model, you need to do several such runs. So the cost can get extremely, extremely expensive.

Laura Florescu: Our belief at MosaicML is that state of the art, large, powerful models should not be limited to just the top companies. As we have seen over the last few years, the costs are getting larger and larger due to both the size of the models, also the data that the models ingest is just exploding.

Laura Florescu: A couple of years ago, a state-of-the-art model, Megatron, actually cost $15 million to train. As you can imagine, startups probably cannot really train models like that. And at MosaicML, this is our belief, that this kind of training should be accessible to other partners as well.

Laura Florescu: That’s where we come in. We want the state-of-the-art efficient ML training. Our co-founders are Naveen and Hanlin from Nirvana and Intel AI. Also, founding advisors from MIT and also founding engineers from leading AI companies. All of us have the same kind of goal to train machine learning basically faster and better and cheaper.

Laura Florescu: Our thesis is, the core of Mosaic is that algorithmic and system optimizations are the key to ML efficiency, right? And then the proof to that so far, is that we are working with enterprises to train ML models efficiently. And we want to enable our organizations to train the best ML models, the cheapest and the most efficient.

Laura Florescu: Some results that we already have. As I said, we want to be agnostic about the kind of models, data that we ingest. For some image classification tasks, we have shown 6x speed ups, like 6x cheaper, faster than like regular training. About 3x faster for image segmentation, about 2x for language models, language generation, and 1.5x for language understanding. We have been around as Julie said for about a year, a year and a few months, and these are already some of the results that we have achieved.

Laura Florescu: A use case is to train NLP models, such as BERT, for those of you who know that, and on our specific platform and without algorithms. Use case is for example, to increase sales productivity. If you see there, on our MosaicML 4-node, with our speed ups, which I’m going to discuss in a little bit, we can see up to 2x speed ups of training such models. And also about 60% training costs reduced by training with both our algorithms and on our platform, on our cloud.

Laura Florescu: The MosaicML cloud, we want it to be the first AI optimized cloud designed specifically for AI and directly to reduce training cost at any layer of the stack. For example, in the training flow, we want to reuse data from past runs. In the models that we’re using, so that’s where the kind of optimized model definitions come in, Composer, which is our open source library. We are doing the algorithmic speed ups, the training. And kind of like at the lower level, we want to also be able to choose the best hardware in order to get to the lowest training cost. At each layer, we are optimizing all of these system optimizations and composing all of those basically leads to the best training runs.

Laura Florescu: As I mentioned, Composer is our open source library. We have a QR code there if you want to check it out. We have about 25+ algorithms that we have worked on and given the name, they compose together, and that’s how we achieve basically the best, 2x to 5x speed ups. And as an example, for a BERT model, we have seen 2.5x speed up for pre-training, which as you can see, goes from nine days to about three days. That’s a huge win. Check it out if you would like. As I said, open source, so we’re always looking for feedback and contributions.

Laura Florescu: We’re open to partnering with any kind of corporate users, for anybody who has vision or language tasks, and then also industry, we want to be industry agnostic and global. Again, we want to optimize basically any kind of models. And as I said, the open source Composer speed up, we’re open to feedback and partnerships for that as well. And of course we are hiring. Yeah, it’s a really great team, really fun, really ambitious. And I’m so honored to work there and we’re looking for all kind of builders, engineers, researchers, products. And thank you very much.

Julie Choi: Thank you so much, Laura. Okay, that was wonderful. Let me just go to the next talk. Our next speaker is Amy Zhang, and Amy comes to us from, she’s currently a research scientist at Meta AI research, and a postdoc I think, you’re not a postdoc anymore, are you?

Amy Zhang: It’s kind of like a part-time thing.

Julie Choi: It’s like, never ending, huh? But you’re on your way to your assistant professorship at UT Austin, amazing, in Spring 2023. And Amy’s work focuses on improving generalization and reinforcement learning through bridging theory and practice. And her work, she was on the board most recently of women in machine learning for the past two years. And she got her PhD at McGill University and the Mila Institute and prior to that obtained her M.Eng. in EECS and dual Bachelors of Science degrees in math and EECS at MIT. Let’s welcome Amy Zhang to the stage. Thank you.

Amy Zhang: Thank you Julie for the really kind introduction and for planning this amazing event. It’s so nice to see people in real life. This is my first large in-person talk in over two years so please bear with me.

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Meta AI Research Scientist Amy Zhang speaks about her career journey in reinforcement learning, from academia to industry, at MosaicML Girl Geek Dinner. (Watch on YouTube)

Amy Zhang: Today I’ll be talking about my research which is in reinforcement learning, but I first wanted to just give a little bit of introduction of myself. To me, I feel like I’ve had a fairly meandering journey through academia and industry and research in general. I wanted to give a little bit of introduction of what I’ve done so that if any of you feel like you’re going through something similar, please reach out and I’m happy to chat and give more details. Like Julie said, I did my undergrad at MIT, a year after I finished my masters, I started a PhD at UCSD. It did not go well, through no one’s fault, really. I just felt really isolated and so after about a year, I quit my PhD, meandered through a couple of startups, and then eventually found my way to Facebook, which is now Meta.

Amy Zhang: At Facebook, I initially started on the core data science team. I was a data scientist, but I was working on computer vision and deep learning. This was 2015, still fairly early days in terms of deep learning and everyone is really excited about the gains that it had shown for computer vision at the time. I was working on population density, so we were taking satellite images and doing building detection to find houses, to figure out mostly in third world countries, what the population density was so that we could provide internet to people and figure out what was the best way to provide that internet.

Amy Zhang: After about a year of that, I ended up joining the Facebook AI research team FAIR, as a research engineer. And after about a year of that, I happened to meet the person who became my PhD advisor, Joelle Pineau, who is now director of FAIR, and I got to do my PhD with her at McGill University while still staying in FAIR. Fast forward a few more years and I defended my PhD remotely in the middle of the pandemic last year and am now back in the Bay Area as a research scientist at FAIR and like I said, was part-time postdoc-ing at UC Berkeley.

Amy Zhang: After spending all this time in industry, and having a really great time in industry, and getting all of these amazing opportunities to do my PhD while in industry, with all of the nice resources that that provides, I decided to go back into academia. And, last year I was on the faculty market, on the academic faculty market, and I accepted a position at UT Austin. I will be starting there next year as an assistant professor.

Amy Zhang: With that, I’m just going to jump straight into my research. And this is going to be, again like I said, a very whirlwind, high level overview. I’m passionate about reinforcement learning. I love the idea of agents that can interact with the world, with us, and it can grow and learn from that interaction.

Amy Zhang: Okay, thinking a little bit about what reinforcement learning (RL) can do. I’m personally really excited about the idea of applying reinforcement learning to solve real world problems. To me, this is personalized household robots, having a robot that will do your dishes, clean the house, make your bed, learned autonomous driving so you can just drive in a car without having to actually drive and pay attention to the road, and personalize healthcare, so having like a robo-doctor who knows everything about you and can personalize healthcare and give recommendations for you specifically.

Amy Zhang: Unfortunately we are not there yet, as I think maybe all of us can tell. Deep reinforcement learning has had a lot of successes in the last two years. Maybe some of these things are familiar to you. AlphaGo, where we have an RL agent that was able to beat the best human experts. OpenAI with playing video games, which I’m not very familiar with, but like Dota and StarCraft. These are the things that have been hitting the news in terms of what Deep RL is capable of. But there are still a lot of disappointing failures and I think none of these videos are going to show, but imagine this robot trying to kick this ball and then just falling flat on its face. That’s what that video is. And in the other little cheetah looking thing is supposed to be like tripping and falling. Anyway, this is where we are currently with RL.

Amy Zhang: Why do we still see this discrepancy? How are we getting these amazing gains but still seeing these failures? And what we’re really seeing is that Deep RL works really well in these single task settings, in simulation, when you have access to tons and tons of training data, but it works less well in visually complex and natural settings. Basically we’re not seeing the same type of generalization performance that we’ve been getting out of deep learning in computer vision and natural language processing. My research agenda is mainly about how can we achieve RL in the real world? How can we solve these problems? And, to me, it seems that abstraction is one key to generalization. And I use this type of idea to develop algorithms that have theory-backed guarantees.

Amy Zhang: I’m going to not really talk a little bit about this math, but I’m particularly interested in being able to train reinforcement learning agents that can solve tasks from pixels. Imagine that you have this household robot or this autonomous driving car that is receiving information about the world through a camera, through RGB input, and that’s a big part of how we also perceive the world. There are things in this image that are relevant for the autonomous driver here and there are things that are not. And we want to figure out how can we determine from just a reward signal, what things are relevant versus irrelevant.

Amy Zhang: I’m just going to, as part of this project, we developed this representation learning method using this idea by simulation, and showed that in this type of simulation driving task, which is done in this platform called Carla, so we have just this figure eight simulation environment where this car is just driving along this highway, and there’s lots of other cars in the road and basically, it’s designed to try and drive as quickly as possible. Using the break as little as possible and maximizing throttle while also not hitting anything. And we find that our method which can basically ignore these kind of irrelevant details and figure out what things are irrelevant does much better compared to a lot of existing methods.

Amy Zhang: One really cool thing is that when we look at the representation that we actually learn here, and we look at what kind of observations get mapped to be close together in this representation space, so what information is actually getting captured by this representation, we see that… this is the agent’s point of view. We see that, in these three examples, you’re always on this straight road where you have an obstacle on the right hand side. It doesn’t matter what the obstacle is, but the representation just captures that something is there, which means that you can’t turn right. This is just kind of an example of what our algorithm can do. And unfortunately I’m going to skip over this, because these were just some videos showing what our agent can do.

Amy Zhang: I just wanted to end on talking about some open problems that I’m particularly excited about. I’m particularly excited about compositionality. How can we solve combinatorially difficult problems. And these correspond to a lot of real world tasks that we should care about. When we think about really simple versions of problems like this, you can have a block stacking task. You can have any number of blocks or any combination of blocks and so you can always have new environments that you give to your agent to try and solve.

Amy Zhang: Similarly, moving more towards actual real world problems that we care about. Again, going back to the dishwasher example or an agent that is trying to move boxes around in a warehouse. These are all settings where the exact environment, the exact state that the agent has to deal with, is constantly different. The objects that you want to place in your dishwasher on a day to day basis is always going to look different. How can we get agents that can actually generalize to all of these new states?

Amy Zhang: I think one really exciting direction to go when trying to solve this type of problem is to think about factorization. How can we break down a problem into smaller, easier building blocks? So if we understand how one block, the dynamics of one block moves, so creating a stack of two blocks, right? Babies play around with this sort of thing and then as they get older, they automatically can extrapolate to building like gigantic towers and castles. So how can we take that idea and give it to reinforcement learning? So this is something that I’m particularly excited by.

Amy Zhang: I just wanted to end talking about my sort of wider research agenda. Now that I’m starting as faculty, I have to start recruiting a group. If any of you are interested in doing a PhD at UT Austin or know anybody who is, please send them my way. But when I think about what my research group does, I’m particularly interested in these three applications of reinforcement learning.

Amy Zhang: The first is in robotics, trying to solve manipulation tasks. Going back to that block stacking example, trying to solve locomotion and navigation tasks. How can we build an autonomous driving system purely from first principles, like end to end machine learning? Reinforcement learning has to be a part of that.

Amy Zhang: Natural language processing, so using RL for text generation, being able to extract knowledge from text, when you build an interactive agent, how do you give it information about the world? We learn from textbooks, we don’t want an agent just deployed in the real world with no basic information. Healthcare, how do we build RL agents that can help out with diagnosis and treatment and tackle a lot of the problems that we have there? That’s basically it, very grandiose. I probably won’t make much progress on a lot of these fronts, but this is the dream. And thanks for listening.

Julie Choi: Thank you so much. I think this is very grandiose and amazing that you’re working on it. Amazing. Thank you, Amy, okay.

Julie Choi: We’re going to just clean some of this up. Okay. And we’re going to open up our next talk, which I’m extremely excited about. Let me introduce our next speaker. Our next speaker journeyed probably the furthest to join us tonight… all the way from the east coast, North Carolina, to be here today to deliver this talk. I want to thank you. Thank you.

Julie Choi: Tiffany Williams is a Staff Software Engineer at Atomwise working on AI-powered drug discovery. Prior to Atomwise, Tiffany was a Staff Software Engineer at Project Ronin where she was developing cancer intelligence software. Tiffany earned her PhD in cancer biology from Stanford University and her Bachelor’s in biology from the University of Maryland. Let’s give a warm welcome to Tiffany Williams.

Tiffany Williams: All right. Hello, everyone. I’ll admit, I have some notes here so I can stay on track, but I’m really glad to be here this evening. I was searching through my inbox and realized that I attended my first Girl Geek Dinner in April of 2015. I was fresh out of grad school and a coding boot camp. Eager to form connections, acquired some cool swag, and to be honest, eat some free food and have some drinks.

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Atomwise Staff Software Engineer Tiffany Williams discusses the drug discovery process with AtomNet at MosaicML Girl Geek Dinner. (Watch on YouTube)

Tiffany Williams: I went to grad school to study cancer biology. My research was from the perspective of a molecular biologist exploring the role of a protein as a target for therapy, and skin cancer. And my interests have generally been at that sweet spot, leveraging data, and technology to improve human health. Coming from a biology research background, in my current role as a software engineer at Atomwise, I’m able to look further down that drug discovery process. I’m happy to share with you all today, what that looks like.

Tiffany Williams: Now, I’ll start by giving a very brief primer on the current state of the drug discovery process, as well as a very brief primer on biochemistry. From there, I’ll talk about what we’re doing at Atomwise to make a significant impact in human health, and some interesting challenges ahead of us. I do want to give a disclaimer that I am merely scratching the surface of what could be discussed in drug design, drug development, and even applying ML on top of that. But what I hope you all take away from my time with you all this evening is just general excitement about the possibility of improving human health.

Tiffany Williams: Beyond that, I hope you feel empowered to do even more digging into the drug discovery process, and maybe you’ll feel empowered to even… to find opportunities to make an impact in that space. For me personally, I’m coming from the east coast, but I have a personal attachment to improving the drug discovery process. I’m on the east coast serving as a caregiver now – my mom has endometrial cancer and it’s at a point where there’s only one treatment option. If any of you have ever been in, unfortunately, in a predicament like that, it sucks.

Tiffany Williams: There’s a lot of data out there, technology’s improving, and it would be great if we can leverage that to improve health, right? This diagram depicts the drug discovery process from the basic research to identify a potential drug target all the way to FDA approval. On the right, I’ve noted the average number of years of the different stages in the drug discovery process. In the middle, what it shows are like basically in these different stages, there are certain types of experiments that are done that basically kind of knock out the potential candidates in that step.

Tiffany Williams: Initially you might have like a candidate pool of over like 10,000 drugs, but in an early stage, which is known as like kid identification, computer simulation is used to predict potential drugs ability to bind to a target of interest. These subsequent steps will test for other characteristics like potential toxicity, or efficacy in cell cultures, and animal models. Eventually, hopefully, we end up with a few candidates that reach the human clinical trial phase to verify safety, and any other side effects along with a few other things.

Tiffany Williams: What I hope that you take away from this slide really is that the current basic research to FDA approval drug process takes a really long time. It takes on average, it’s estimated to take about 15 years, and it’s also really expensive for each drug that goes to market, it’s estimated that 2.6 billion is spent.It’s the case that not all of these trials have a happy ending. For every drug that makes it to the market, millions may have been screened, and discarded. We have to improve this process, right? But in order to appreciate how this process can be improved, let me first give that very, very, very brief biochem primer. And I’ll focus specifically on protein interactions.

Tiffany Williams: Up to this point, and then even later in this talk, I’m going to be using some words interchangeably, and I just want to make sure I bring some clarity to what I’m actually talking about. When I say like ligand, or ligand, I’m specifically referring to any molecule that combined to a receiving protein, and that receiving protein is also known as a receptor. And in this presentation, when I use the word or phrase drug candidate, I’m actually referring to that ligand and the receptor would be that the drug target.

Tiffany Williams: One model for how proteins interact is this lock and key model. And the gist of this is that the ligand and the receptor have these somewhat complimentary sites. And the ligand combined to this complimentary site or active site, and basically alter the shape, and or activity of the receptor. And one more thing is that this binding is also referred to as docking. If we know our bodies are made up of proteins, and they have a diversity of functions within the body, but in a disease state, a normal process can become dysregulated. This image on the far right, is taken from a classic cancer biology paper called The Hallmarks of Cancer. The premise of this is that there about 10 biological capabilities that cells take on as they like morph into this more cancerous state. And I’m not going to go over all of these, but I want to highlight that two of these biological capabilities would be like cell growth and like cell motility.

Tiffany Williams: Those are normal functions within the body. But I guess within a diseased state you’ll have overgrowth, or you may have cells that are primarily concentrated in one area, develop the capability to invade into other tissues or metastasize. I say all this to say that like in small molecule drug discovery, what we want to do is actually figure out what sort of structure is needed for a ligand to bind to this problematic protein or proteins, and counteract that like not so great behavior. If this is where Atomwise comes in, right? Or it does, if you don’t know. We want to know how can we efficiently explore this space of all potential chemical compounds to better identify small molecule drugs faster.

Tiffany Williams: Atomwise actually developed AtomNet the first deep convolution neural network for structure based drug design, so that we can actually make better predictions for potential drug candidates earlier in the drug discovery process and faster. I’m highlighting this paper, again, just trying to give high level overviews. Feel free to check this paper out, but what I want you to take away from this is that the AtomNet technology is currently being used in real drug research, in cancer, neurological diseases, various spaces, on the right is, it’s actually a GIF, but since it’s a PDF, it’s not showing up as a GIF, but basically this GIF would show the AtomNet model. It would simulate the AtomNet model, predicting candidate treatments for Ebola. And this prediction that AtomNet made is actually has led to candidate molecules that are now being studied in animal models.

Tiffany Williams: One, despite everything I’ve said up till now, you actually don’t need a background in wet lab research, or chemistry to appreciate what’s happening here. The power of convolute or the power of convolution neural networks, or CNN, is that it allows us to take these complex concepts as a combination of smaller bits of information. And I think if you’re familiar with CNN, or even if you don’t like one area that is really popular, has been computer vision.

Tiffany Williams: I’ll briefly go over like an example of image recognition, and then kind of like try to tie it into how AtomNet works. An image is essentially represented as a 2D grid with three channels, you have red, green, and blue. And this network learns images of objects or faces, for example, by first learning a set of basic features in an image like edges. Then, from there, by combining those edges, the model can then learn to identify different parts of that object. In the case of a face that might be ears, eyes, nose, et cetera. With AtomNet that it’s working in a similar fashion, that receptor ligand pair is represented in a 3D grid, and the channels are essentially the elements that make up protein like carbon oxygen, nitrogen, et cetera. In the case of AtomNet, that the learning of edges is actually the learning of the types of, or predicting the types of bonds between those elements.

Tiffany Williams: Then from there, the compliment to the ear eye detection would be actually identifying more complex molecular bonds. You could say, essentially, that this network is like learning organic chemistry 101. This is powerful because we can then train these models to make predictions about different aspects of the drug discovery process. Like what ligands, or what type of structures are most likely to bind to a certain target? At what strength? What are the additional modifications that we can make to a potential drug candidate to strengthen that bond? Beyond that, it’s not enough just for the ligand to bind to the receptor, it also has to be like biologically relevant in that, let’s say, if we’re looking for something that is treating some neurological disease. We need that ligand to be small enough to cross the blood brain barrier. Or we may need to take into account toxicity or other effects that may happen in the body. Metabolism. We don’t want that small molecule to become quickly metabolized in the body before it has the opportunity to have the intended effect.

Tiffany Williams: These are exciting times, and I’m really, really passionate about the work that we’re doing at Atomwise and any work that is being done at the intersection of health and technology. I wanted to briefly go over some of the projects that my team is currently working on. I work on the drug discovery team at Atomwise, it’s within the engineering team. I think we’re working on some pretty interesting issues. One of my, my teammates Shinji, he recently has been working on bringing best engineering practices, and improving performance of some of our ML tools. Adrian, my teammate, is working on optimizing algorithms to explore a three trillion chemical space. He is also been working on, or has been able to create simulating mocular… mocular, I’m combining words… molecular docking on GPUs.

Tiffany Williams: Then another thing that we’re working on that I actually have more of a hand in, is building a research platform to better enable drug discovery. Oh, I didn’t mention the third person Shabbir, who’s our manager, and he has his hands in a bit of everything. What I hope you take away from this is that I think we’re at an exciting time in today to like really leverage data, and technology to make a major impact in human health. I think there are a lot of challenges, interesting challenges in drug discovery. I hope that you may have been convinced that you actually don’t need a background in chemistry to contribute. There are a lot of transferable skills. If you just know software engineering, or you know ML, or if you’re in product or marketing, there is a place for you in this space.

Tiffany Williams: Finally, really important takeaway is that we are hiring. My team alone, please, if you have any front end experience, if you have backend experience, or if you have a background in computational chemistry, those are some of the positions that are open right now on my team, but then outside of my team, we also are hiring. Definitely check out our careers page. If you have any more questions or are interested in chatting, feel free to reach out to me. I have my LinkedIn handle as well as my Twitter handle, posted here. Then finally, I think I mentioned that I had some references to share. Again, I’m only scratching the surface. There’s so much information out there. I wanted to highlight two Medium articles that were written by former Atom, which is what we call people that work at Atomwise, machine learning for drug discovery, in a nutshell, I highly recommend starting there if you want to do a deeper dive. That’s it.

Julie Choi: Thank you so much, Tiffany. Yes. I think drug discovery is an incredible application domain for deep learning. Really appreciate your talk. Okay, let me introduce our next speaker. Our next speaker is Shelby Heinecke. Shelby is a senior research scientist. Again, I did not touch. Okay.

Julie Choi: Shelby is a Senior Research Scientist at Salesforce Research, developing new AI methods for research and products. Her work spans from theory-driven, robust ML algorithms to practical methods, and toolkits for addressing robustness in applied NLP and recommendation systems. She has a PhD in Mathematics from the University of Illinois at Chicago, and a Master’s as well in Math from Northwestern and her bachelor’s is from MIT. Let’s welcome Shelby.

Shelby Heinecke: Thanks so much. Awesome. I have to give a thank you to Julie for including me in this event, inviting me. This is my first Girl Geek, and not my last Girl Geek event. I’m super excited to be here. Yeah, let me get started.

mosaicml girl geek dinner shelby heinecke speaking recommendation systems salesforce research

Salesforce Research Senior Research Scientist Shelby Heinecke speaks about how to evaluate recommendation system robustness with RGRecSys at MosaicML Girl Geek Dinner. (Watch on YouTube)

Shelby Heinecke: Today I’m going to be talking about evaluating recommendation system robustness, but first I feel kind of like the new kid on the block. Let me just give a little bit of background about myself. I moved here to the Bay Area about a year and a half ago in the middle of the pandemic. Super excited to be here in person to meet people.

Shelby Heinecke: Currently I’m a Senior Research Scientist at Salesforce Research. As Julie mentioned, I work on both research and product. It’s pretty awesome to develop prototypes, and see them in production. Before I was here at Salesforce in the Bay Area, I was doing my PhD in math in Chicago. There, I focused on creating new ML algorithms that were robust. I worked on problems in the space of network resilience. Before that I got my master’s in math, kind of focusing on pure math at Northwestern. Originally I hail from MIT Math, focusing on pure math there. That’s my background. Today’s talk recommendation systems and robustness.

Shelby Heinecke: Let’s get started. A crash course and recommendation systems. So, what is a recommendation system? Well, it consists of models that learn to recommend items based on user interaction histories, user attributes, and or item attributes. Let me give you an example, say we want to build a recommender system that recommends movies to users. Well, what kind of data can we use? We’re going to use the users, previous movies. They viewed we’re going to use the ratings that they rated those movies.

Shelby Heinecke: We’re probably going to take a look at the user’s age. We’re going to look at the user’s location. We’re also going to take into account item attributes. The movie attributes, like the movie title, the movie genre, we’re going to take all that. That’s all of our data, and we’re going to train models. The models can build the recommendation system. As you can imagine, recommendation systems influence our daily lives. We’re all exposed to recommendation systems every day. Just think about purchases. Think about movies.

Shelby Heinecke: Think about songs you’re recommended. Think about the ads that you see every day, people, news, information. We are at the mercy of these recommendation systems and a lot of our decisions are highly influenced by what the recommendation systems decide to show us. Let’s think about the models that we see in recommendation systems. Models can range from simple heuristic approaches, like a rules based approach or co-sign similarity approach to complex deep learning approaches, think neural collaborative filtering, or even now we’re seeing transformer based approaches coming to light.

Shelby Heinecke: With the vast range of models available and how greatly they impact our daily lives, understanding the vulnerabilities of these models of each of these types of models is super critical. Let me get started about recommendation model robustness. As we all know, machine learning models are trained on data, and ultimately deployed to production. And in that process, there are some hidden sources of vulnerabilities that I want to bring to light.

Shelby Heinecke: One of the big issues is that training data may not reflect the real world data. In many cases, we’re training on data that’s been highly curated. That’s been cleaned up. And yeah, and so because of that, when we train a model on that very clean, highly curated data, it’s not going to necessarily perform well when it’s exposed to the messy data of the real world. Real world data has noise. Real world data is just can be unpredictable. As a result, sometimes we train model, we train a recommendation model, but we see poor performance and production. That’s definitely one type of vulnerability we need to watch out for.

Audience Member: Woo! So true.

Shelby Heinecke: What? Okay. Another type of vulnerability. I love the enthusiasm, okay? Another type of vulnerability. As you can imagine, recommendation systems are closely tied to revenue for a lot of different parties for companies, for sellers. There’s an issue that participants may intentionally manipulate the model. Think about creating fake accounts, trying to do things, to get your item higher on the list for customers, things like that. That is a reality and that’s something we have to take into account.

Shelby Heinecke: The last thing I want to bring to light is poor performance on subpopulations. This is a well known issue across all of ML, but I just wanted to bring it to light, to recommendation too, that when we train models and we test on the evaluation set, usually the basic evaluation methods think precision recall F1. We’re computing that on the entire test set, so we’re averaging overall users. And in that average, sometimes we’re hiding…

Shelby Heinecke: Sometimes there’s poor performance on subpopulations that are hidden. For example, a subpopulation that you may care about could be new users, or maybe a users of a certain gender. That’s something that we just need to keep an eye on. We don’t want poor performance on key subpopulations I’ve told you about all these different types of vulnerability.

Shelby Heinecke: What is a robust model? Well, a robust model you can think of it intuitively as a model that will retain great performance in light of all of these potential perturbations, or in light of all of these scenarios. The question is how can we assess the robustness of models? That is where one of our contributions comes in.

Shelby Heinecke: I want to introduce one of our open source repos called RGRecSys, and it stands for robustness gym for recommendation systems. Our library kind of automates stress testing for recommendation models. By stress testing, I mean, you can pick a model, you can pick a data set, and you can stress test it in the sense that you can manipulate the data set.

Shelby Heinecke: You can add in attacks, you can add in noise and so on. I’ll actually go into more detail about that. And you can see in a really simple way how your model, the robustness of your model. As I mentioned, RGRecSys, is a software, is a software toolkit. It’s on GitHub. I’ll talk about that in a second and it’s going to help you assess the robustness of your models.

Shelby Heinecke: One thing that we contribute is that RGRecSys provides a holistic evaluation of recommendation models across five dimensions of robustness. When I say I told you about various types of vulnerabilities, there’s various types of robustness. Our library helps you to quickly and easily test all these different types of robustness for your model. Using our API, you just simply select a model for testing, and then you specify the robustness test that you’re interested in trying along with the robustness test parameters. What I’ll do is I’ll go over the types of tests that are in the library.

Shelby Heinecke: Let’s talk about the different tests that we have in our library. First is around subpopulations and this kind of goes back to what I mentioned in the previous slides. This will allow you the test. What is the model performance on subgroup A versus subgroup B? And this is going to be very useful because, as I mentioned, most of the time, we’re just computing precision recall these usual metrics on the test set, but this gives you an easy way to test performance on specific subgroups. This could be useful, for example, if you want to test you want to test performance on gender A versus gender B, or new users versus old versus like users that have been in the system for longer time. Just some examples there. That is one type of test you can run in our library. The second type of test is around sparsity. If you think about recommendation systems and you think about the items available like a movie recommender, or a purchasing recommender there’s millions of items, and each user really only interacts with a handful of those items.

Shelby Heinecke: Each user is only clicking, only purchasing, only viewing a handful. This is a source of data sparsity. Data sparsity is a huge problem in recommendation systems. It will be good to test the degree to which your model is sensitive to sparse data. That’s one thing you can test with our library. The third test is around transformations. There are a lot of ways that data can be perturbed when you’re training a recommendation system model. For example, maybe you’re gathering data about your users and maybe that data is erroneous in some ways. And because of that, you might want to test if a recommendation model will be robust, if user features, for example, are perturbed. The fourth test that you can test is around attacks. As I mentioned, there’s a lot of reasons why people would try to attack a recommendation system it’s tied to revenue ultimately.

Shelby Heinecke: What you can do with our library is implement some attacks and test how your model performance would change under those manipulations. And finally distribution shift. What we see is that the training data that you train on is often different from the data that you’ll see in production. It’s super important to be able to know, get a sense of how was my model going to perform if it’s exposed to data from a slightly different distribution? You can go ahead and test that with our library., I definitely encourage you to check out our library on GitHub, and feel free to check out the paper for way more details about the capabilities. And with that, thank you so much for listening. It was great to share it. Feel free to reach out.

Julie Choi: Thank you, Shelby. That was great. It’s very important to be robust when we’re doing model deployment. Okay. Angela Jiang, thank you so much for joining us tonight.

Julie Choi: Angela is currently on the product team at OpenAI. Previously, she was a product manager at Determined AI building, deep learning training, software and hardware, deep learning… I think Determined AI was recently acquired by HPE. And Angela graduated with a PhD in machine learning systems from the CS department at Carnegie Mellon University. And we actually have built some of our own speed up methods on your research. It’s an honor to have you here today.

Angela Jiang: Thank you so much, Julie, for inviting me, for the introduction, as well as Sarah, and Angie for organizing the event and bringing us all together too. Yeah. Like Julie mentioned, I’m Angela. I work on the product team at OpenAI.

mosaicml girl geek dinner angela jiang openai

OpenAI Product Manager Angela Jiang speaks about turning generative models from research into products at MosaicML Girl Geek Dinner. (Watch on YouTube)

Angela Jiang: I work on our Applied team where we really focus on essentially bringing all of the awesome research coming out of the org and turning them into products that are hopefully useful, safe, and easy to use.

Angela Jiang: Most of my day, I’m thinking about how to turn generative models from research into products. I thought I’d make the talk about that as well. This might as well be a list of things that keep me up at night, think about it that way. But in particular, what I really wanted to highlight is just some of the observations I’ve had about things that make these generative models unique and tend to have large implications on how we actually deploy them into the market.

Angela Jiang: To start, I want to share a little bit about what OpenAI’s products are. OpenAI does a lot of AI research. In particular, we do a lot of generative models. Over the last two years, we’ve really started to work to bring those generative models into real products. Three examples here are GPT-3, that does text generation; Codex, that does code generation; and most recently, DALL-E, which does image generation. These were all made by DALL-E.

Angela Jiang: To get a little bit more concrete, what these products are is that we expose these models like GPT-3 and Codex via our APIs. As a user, you can go to the OpenAI website, sign up, and then hit these endpoints and start using these models. And here’s an example of how you might use the GPT-3 model. Here we have an example of your input in the gray box. It might say something like, “Convert my shorthand into a firsthand account of the meeting and have some meeting notes.” And you submit this to the model, and then the model will do its very best to return you an output that completes this text. And in this case, the response is essentially a summarization of your notes.

Angela Jiang: Codex works very similarly, except it’s also specialized for code. And in this case, our input is not only text, but it’s also some code, some JavaScript code, and some hints that we want to transform it into Python. And the output is some Python code that does that. DALL-E is very, very new. It’s been out for around a month, so it’s not available in the API or anything yet. But it has a very similar interaction mode where you have, again, an input, which is a description of the image that you want. And then as the output, it provides some images that relate to that description, which in this case is a cute tropical fish.

Angela Jiang: I think GPT-3, Codex, and DALL-E are all really good examples of taking research and seeing that there’s a really big market need for the kinds of capabilities that they expose. Then working as an Applied team to actually transform that research into a productionized product by essentially figuring out where that user value is, designing the product so that we can deliver that user value in a way where users are set up for success, making sure it’s deployed responsibly, et cetera, et cetera.

Angela Jiang: To date, there’s hundreds of applications that have been submitted for production review that is built upon this API. Those are applications like writing assistants like CopyAI if you’ve heard of that, coding assistants like GitHub’s Copilot is built on top of Codex, as well as a lot of other applications like chatbots for video games, question answering bots, etc. This is pretty good validation that these models are not only really exciting research, but are also solving problems in the market.

Angela Jiang: Now, when we think about bringing these to market and turning them into real products, I think there are two interesting properties of these generative models that really change who uses them as well as how we actually deploy them. I’ll talk about how these models are stochastic and how they’re very general. With typical products, you might expect that the results are very predictable and deterministic. For instance, if you’re coding up a payments API, every time you use that payments API, you’d expect it to react in a similar way. This is not how our models work at all.

Angela Jiang: Our models are probabilistic. Every time you submit a prompt, you could get a different response back. If you tweak that prompt ever slightly, then you could get a very different response back. That really changes how you interact with a product and what kind of applications can be built on top of it. Another difference is I think that typical software products that I think about are typically focused on solving a need or a problem very directly, whereas, in these generative models, they’re very, very general. They’re capable of doing a lot of things all simultaneously.

Angela Jiang: For instance, you would think that GPT-3 would be able to summarize legal texts and also simultaneously write poems or maybe even code. I think this is super exciting from a science perspective as we get more and more powerful models, closer and closer to something that’s super general. But from a product perspective, this comes with some challenges because we have to now take one model and one product and have it serve many, many use cases simultaneously.

Angela Jiang: For the rest of this talk, I’ll talk about, again, more detail about how these two properties affect the way that we deploy our products, and I’ll give some examples of each. Okay, starting with stochasticity. Right. Our models, again, are probabilistic, which means that every time you use it, you might get a different result. The result might not be what you want or maybe the result is what you want every third time you try the model.

Angela Jiang: It might be surprising that you can actually build real applications on top of this kind of behavior. What we actually find is that for a lot of tasks, especially very simple tasks, we can have very concrete and reproducible behavior for even tasks where the outputs are imperfect or are only correct some of the time, these results are actually still very useful for many applications in the right context.

Angela Jiang: We find that these models work really well, for instance, in creating productivity tools where there’s a human in the loop. A really good example of this is Codex Copilot. Some of you may have used it before, but this is a screenshot of it in action, where it’s a Visual Studio Code extension and it just gives you auto-complete code suggestions that the user can then tab to accept or reject.

Angela Jiang: This is great for a product like Codex because you have actually an expert there that’s telling you, is this completion good or not? And generating more. What we actually see is that most of the suggestions from Copilot are rejected by the user, but still, developers tell us that this is an integral part of their deployment pipeline or their development pipeline I should say. It does not need to be perfect every time we generate something. Those are cases where you don’t need a perfect result every time. What you’re looking for is just inspiration or getting over writer’s block. In some of the applications built on top of these APIs, you actually don’t have a correct answer to shoot for.

Angela Jiang: Applications that are doing art or creative tasks or entertainment, that’s a case where having variety really is helpful actually. Here I have a DALL-E prompt, which is a bright oil painting of a robot painting a flower. And here are different generations from DALL-E. And in this case, it’s really helpful actually to have probabilistic nature because, for one, I want to have different options. And it’s also really cool to have generations that you’ve never seen before or other people haven’t seen before.

Angela Jiang: I think it’s even counterintuitive that, in this case, we’ve actually learned that these AI systems seem to be doing really, really well and surprising us in creative tasks as opposed to rote tasks, which you might have expected the opposite earlier. Those are some examples where the properties of these generative models affect what applications are built on top, but it also affects how we deploy these models. Going into a little bit more detail, one property is that these products are really, really hard to evaluate.

Angela Jiang: This makes being a Product Manager, among other things, quite challenging because we really need to know how our products perform so we can position them in the correct way to the users, know what to deploy, and tell users how to use them correctly. For example, we have these text models, GPT-3, and they have different capabilities, right? One capability is they might be able to complete text, and another capability is they might be able to edit existing text. There’s some overlap there on which one you should use.

Angela Jiang: It’s really important that we understand exactly how this model performs on different tasks so that we can direct users to use the right tool at the right time. We try really hard to figure out creative ways to evaluate these at scale, given that we often need a human in the loop to be telling us if a generation is good or not.

Angela Jiang: We do things like large-scale application-specific A/B testing. Like we see if we use one model or the other, then which model gets more engagement for this writing assistant.

Angela Jiang: Next, I’ll just give a couple of examples of how generality also comes into the picture here. Like I mentioned, these models are capable of a lot of things simultaneously because the way that they’re trained is that they’re trained for a very long time, for months, on a lot of data. And the data spans the internet, books, code, so many different things.

Angela Jiang: By the time we get this model as the Applied team, we’re really still not sure what kind of capabilities it’s picked up at that time. But like I mentioned, it’s really important that we understand that quickly. This is also really exciting because, at any given time, we can always discover a new capability of the model, and it’s often discovered after training.

Angela Jiang: An example of this is that the original GPT-3 model was built with text for text. And it was really a discovery after the fact that it also happened to be kind of good at code as well. And these signs of life and discovery is what ultimately sparked this idea of a Codex series that specialized in code.

Angela Jiang: As a Product Manager, it’s really critical that we keep on discovering and probing these models to really understand what’s the frontier of what they can do. And again, we are still figuring out what is the best way to go about this. But something counterintuitive that I realized after I started is that traditional user research that we’re used to doesn’t actually work really well for this use case because you might expect that you would go to your users and ask, how is this model? What can you do with it?

Angela Jiang: What we find is that our users, by definition, are working on tasks that the model was already really capable at. Their focus is not on the new capabilities of the model and pushing that frontier oftentimes. What’s worked better for us is working with creatives or domain experts to really hack and probe at the model and see what the limits of it are.

Angela Jiang: That’s the exciting part of generality, that there’s always a new capability around the corner to discover. But there’s also some risk to it because there’s not only all these capabilities in the model, but the model can also generate things that are not useful for you, and it can also generate things that you really don’t want to generate.

Angela Jiang: For instance, GPT-3, when prompted in the right way, can generate things like hate speech, spam, violence, things you don’t want your users to be generating. This is also a big part of our goals as a research and applied organization is to figure out how to deploy these models in a way that doesn’t have toxicity in them.

Angela Jiang: There’s a lot of different approaches for doing this, spanning from policy research to product mitigations to research mitigations, but I’ll highlight two things here. One thing that’s worked really well for us is actually fine-tuning these models after the fact with a human in the loop, telling you exactly what kind of content is good content.

Angela Jiang: What we found by doing this is that we have a much better time at having the model follow the user’s intentions. And we have a much less toxic and more truthful model as a result of it. At this point, every time we deploy a model, we also fine-tune the language model in this way. We also do a lot of research and provide free tooling to help the users understand what the models are generating at scale so they can understand if there’s anything that they need to intervene in.

Angela Jiang: Okay. Finally, even though I think it’s very, very cool that these models can do a lot of different things at once, sometimes it really just can’t serve multiple use cases simultaneously well. Different use cases often will just require, for instance, different ways of completion.A chatbot for children is going to want a very different personality than a chatbot for support.

Angela Jiang: You also just have different product requirements in terms of accuracy or latency or even price. Copilot is a really good example of something that needs an interactive latency so that it can continue to be useful in real time. But contrast that with something like SQL query generation, which doesn’t need a fast latency, but actually just wants the most accurate response that you can give it. What we’ve found is that the combination of two things have allowed us to provide this flexibility to serve the use cases that we need to serve.

Angela Jiang: One is that we don’t just expose the best model that we have or the most accurate model we have I should say, we expose models with different capability and latency trade-offs for the users to choose from. And then we also offer fine-tuning as a first-class product so that you can take a model and then fine-tune it so that it has your personalized tone or your personalized data bank.

Angela Jiang: These are, hopefully, examples that give you a flavor of what it’s like to deploy generative models. This is also just the tip of the iceberg. If any of this stuff is interesting, please feel free to come and chat with me. I should also mention that we are hiring. We are. Thank you so much.

Julie Choi: Thank you so much, Angela. After DALL-E was launched, the productivity went a little down. We were so distracted by the DALL-E. I don’t know whether to thank or curse your team for that.

Julie Choi: Thank you, Banu. It is just a joy to introduce our next speaker. She’s a friend of mine and a former colleague, really an all-star. It’s so wonderful to have you here, Banu. Thank you.

Julie Choi: Banu Nagasundaram is a machine learning product leader at Amazon Web Services where she owns the go-to-market strategy and execution for AWS Panorama, an edge computer vision appliance and service. Prior to AWS, Banu has spent over a decade in technology, building AI and high-performance computing products for data centers and low-power processors for mobile computing. Banu holds a Master’s in EECS from the University of Florida and an MBA from UC Berkeley’s Haas School of Business. Let’s all welcome Banu.

Banu Nagasundaram: Thank you, Julie and team for having me here. I’m super excited to be here. One difference from the other speakers is this is not my area of expertise, the title of the talk. It is something I’m trying to do better at that I wanted to share with you.

mosaicml girl geek dinner banu nagasundaram speaking aws

AWS Product Manager Banu Nagasundaram speaks about seeking the bigger picture as a ML product leader at MosaicML Girl Geek Dinner. (Watch on YouTube)

Banu Nagasundaram: I’m trying to seek the bigger picture at work. I’m a Product Manager and I’m trying to see why companies do what they do and learn more in that process. What I want you to take away from this is how you can also seek the big picture in your roles that you do either as engineering or product leaders.

Banu Nagasundaram: With that, I wanted to share a little bit about the companies that I work with on a daily basis. These are concerns who use machine learning and AI and work with AWS to implement the services in production. This is different from the research that we spoke about.

Banu Nagasundaram: These companies are looking at getting value out of these systems that they put in place, of course, based on the research, but taking into production. What I implore you to think about is put on a hat of a CTO or a CIO in each of these companies and think about how and why you would make the investment decisions in machine learning and AI.

Banu Nagasundaram: For example, I work with healthcare and life sciences team. I learned a lot from the drug discovery talk earlier from Tiffany here, but I do work with healthcare and life sciences team to understand how they can take the vast amounts of health data that they have to translate into patient information that they can use to serve patients better.

Banu Nagasundaram: They use multiple services to personalize, to extract value from the text data, a lot of unstructured data that they have. That’s one category of customers.

Banu Nagasundaram: The second type of customers that I work with include industrial and manufacturing. The key component that they’re trying to improve is productivity and also optimizing their manufacturing throughput.

Banu Nagasundaram: The questions that they ask and they seek to improve include automating visual inspection. How can I improve the product quality across my manufacturing sites? I have thousands of sites in the US. I scale globally. How can I implement this process not only in one site but uniformly across those thousands of sites to achieve something like predictive maintenance on the tools, improve uptime of their equipment, etc?

Banu Nagasundaram: Third set of customers we work with include financial services. They are looking at data to improve or reduce the risk in the decisions that they make. They’re trying to target customer segments better so they can understand underserved populations but lower the risk in making those products and offerings that they want to do.

Banu Nagasundaram: They also look into fraud detection and many applications around financial services. Retail, this is one I work closely with because I work in the computer vision team right now. And retail is trying to use the insights from computer vision products to see how they can reduce stockouts, which is basically when you go to the store, is the product available? Can they sell it to you? How can they manage inventory? Can they keep track of the count or the number of people entering the store?

Banu Nagasundaram: You may have heard about Amazon Go, for example, a store with just a walkout experience. A lot of retail companies are working with us to understand how they can use computer vision to build experiences like that.

Banu Nagasundaram: And it doesn’t stop at a retail store. Think about the operations officer, a centralized person who’s sitting and trying to analyze which region should I invest more on? Which region should I improve security? Should it be in North Carolina? Should it be in California? Or should it be in a whole different country? They’re trying to collect and gather insights across their stores regionally, nationally, internationally to make those decisions.

Banu Nagasundaram: Then there’s media and entertainment too, which we touched upon a little bit around recommendation systems and personalization. Here we work with customers who are looking to improve monetization, who are looking to create differentiation in the marketplace, in the very highly competitive media and content marketplace, through those recommendation systems and personalization that they have.

Banu Nagasundaram: Across all of these customers, the core task as a product manager that I work on is understanding their requirements and then translating it into product features so that they are served better. But what I learned in the process is that it’s so much more in decision-making than just understanding about features or product requirements.

Banu Nagasundaram: It’s about what enables these CTOs to make those decisions is value creation. How can they use all of these AI ML systems to realize value from the systems that they put in place? One simple way to think about what value creation is, is that it’s an aggregation of data, analytics, and IT that brings the machine learning together. But there’s a second part to it, which includes people and processes.

Banu Nagasundaram: What I mean by that is all of this analytics and machine learning and data can help them understand something, but they still have to lean on people to analyze the data that they gather, make improvements in the process in order to recognize and create value. And that’s the workflow for decision-making across all of these companies.

Banu Nagasundaram: We can look at this as two buckets, one in data analytics and IT, and the second one as people and processes. For the computer vision product that I own, I wanted to talk to you about the value chain for the first part, which is the data analytics and IT portion.

Banu Nagasundaram: This might look a complex set of boxes. It is. Once I finish the pitch here, if this excites you, I’m hiring too, growing my team. Hopefully, I do a good job in explaining this value chain.

Banu Nagasundaram: I started my career in the bottom left, by the way, in silicon processor design. Pretty much in the bottom row, trying to understand how silicon design is then aggregated into components and how distributors sell those components to OEMs who are equipment manufacturers, and how from those equipment manufacturers, the equipments reach the consumers, which is through those equipment distributors.

Banu Nagasundaram: My product is currently both an appliance and a service. I do start from the silicon side, working with partners, I can give examples like Nvidia on the silicon side, Lenovo on the OEM side. And then once you have these equipment distributors selling these devices or equipment I should say to infrastructure providers, one of the examples of infrastructure providers is cloud service providers, but it can also be on-prem equipment providers.

Banu Nagasundaram: You then go to the infrastructure providers, which is the second row from the bottom, and these infrastructure providers then build the tools and frameworks either themselves or through the partner ecosystem. Those tools and frameworks essentially put in place to make efficient use of that underlying infrastructure. It’s a motivation for these infrastructure providers to offer the best tool and frameworks so that you can gain that value out of that underlying infrastructure.

Banu Nagasundaram: Then comes the ML services. You have the overall MLOps flow is what would fit in this bucket. Once you have the tools and the frameworks, how can all of these pieces get grouped together to build a robust system that can scale in production? This goes from data annotation, labeling, training to predictions, to model monitoring. “How can you maintain this when it is in production” is a key question for these decision-makers.

Banu Nagasundaram: Then comes the AI services that are built on top of these ML services. AI services can either be services offered by CSPs or it can be services offered by startups or companies who are trying to build the microservices or services on a specific use case. This is where the customer use case comes into play, where you have that specific use case. In case of computer vision, you can think of services like Rekognition or Lookout for Vision. Those are two examples that AWS offers for computer vision services.

Banu Nagasundaram: Then comes the final layer. This is the layer that the customers that I refer to, the CTOs, work closely with. The independent software vendors or ISVs are companies that build these software solutions, aggregating everything that I spoke to you about, but building the software components of it. But the software component by itself is not going to function in a customer’s premise. For the customer to realize value, the solution, the software solution for their use case has to integrate with their existing system. That’s where system integrators come into play.

Banu Nagasundaram: For example, Deloitte. You can think of Deloitte, Accenture, et cetera are system integrators who bring this whole puzzle together for customers, build that reference architecture for that solution. And they’re like, okay, so now we have this architecture in place. Then they bring in the value-added resellers, who are companies like Convergent or Stanley, for example, who take this entire system that’s put in place and roll it into individual sites.

Banu Nagasundaram: This is where it reaches the scale of thousands of sites. Once a solution is put together, proven in a pilot or a production pilot or a proof of concept, when you go to production, across globally, across cities, across countries, the value-added resellers roll the system into place in the customer’s site. But it doesn’t end there. There need to be managed service providers who can offer service contracts to maintain the system in place once it is on a customer’s premise.

Banu Nagasundaram: All of these building blocks in the value chain is what makes ML, machine learning, in production, intangible for a customer to realize value. It is a big journey. And this is the team I’m building who will work with individual partners across this value chain. And if this is something that excites you, we can talk after. In that value chain, we started with value creation. What is it that companies are looking for? We saw the value chain, but the value realization, let’s say the companies went through this process, put this whole system in place.

Banu Nagasundaram: What is it that actually helps them realize that value? When we think about data analytics in IT, many of the ML practitioners that I talk to, talk to me about the output, the visualization, the dashboards, the histograms for decision making, but it doesn’t stop there. That is not sufficient for these companies. There has to be people in those companies who take this output and actually work to achieve an outcome. The outcome is increase in productivity, increasing in throughput. It’s like, if I can know my demand better as a retail store, or if I can forecast the demand better, I can do so much better in my business.

Banu Nagasundaram: That is an outcome that I’m looking for from this data output that I’m getting from the machine learning models. But is that outcome sufficient? No, the outcome is like one place, one time, you are able to visualize that outcome, but you have to scale that outcome globally in order to achieve the impact. The impact that businesses look for is that you have to either increase revenue, reduce costs, reduce their risk, improve their sustainability, or create a competitive advantage. This is what the ML journey in production looks like. While I walked you through incrementally from the beginning on what it would take customers to get there, customers don’t work through individual steps, reach there and see what’s the next step.

Banu Nagasundaram: They actually have to make assumptions along the way and then understand what the impact might be and this is an AWS term that I’m going to throw and work backwards, which is understand what is it that you want and then build out what is it that you can, what you need to do towards achieving that end goal. That’s the big picture that I want to leave you with. In this whole ecosystem of having machine learning in production, can you think big on behalf of the customer, can you seek the big picture that the customer is looking for? If you’re working on a feature, what is the end state of that feature? What is the end state of that business? Who is actually your end customer? Your end customer may not be the team that you’re immediately working with and who is the decision maker for that overall flow?

Banu Nagasundaram: One of the simple frameworks, this might sound silly. It’s super simple, is to ask five whys, which is in a customer discovery or any feature that you’re building. Just ask, why is that outcome important? Why is that output important? How is it going to help? Why is it going to be something that helps the customer? Why is it needed tomorrow? All of these questions is just going to help drive a little bit more clarity into the bigger picture and motivations for the customer on how they make investment decisions and choices in your particular products and features that you’re building. And that’s it. I want to leave you with one fair warning. If you try that five why’s with your partner, that’s on your own.

Julie Choi: All right. Thank you. Banu, that was great. Thank you so much. It is my honor to introduce Lamya Alaoui, a dear friend of mine and I’m so thankful to you, Lamya, for agreeing to give the closing talk of the night. Lamya is currently Chief People Officer at Hala Systems, and she has been committed throughout her career to supporting organizations as they shift behaviors to align their talent strategies with their business objectives. Her corporate background includes over 15 years of experience in talent acquisition and management, where she has had the opportunity to build teams for companies such as Bertelsmann, Orange, Groupon, Google and Microsoft. Her work experiences span North America, Europe, Asia, Middle East, and North Africa. Let us welcome Lamya Alaoui.

mosaicml girl geek dinner lamya alaoui speaking hala systems

Hala Systems Director of People Ops Lamya Alaoui talks about 10 lessons learned from building high performance diverse teams at MosaicML Girl Geek Dinner. (Watch on YouTube)

Lamya Alaoui: Thank you so much, Julie. Hi everyone. Thank you. I don’t see Sarah here, but I want to thank her personally because she has been so patient and Angie as well for inviting me here. How is everyone feeling today? This is the non-tech talk. This is the people talk. A quick background before we get into it. At Hala System we develop early warning systems in war zones. Not the type of things that you broadcast usually when you are in this type of settings, but we’re hiring as well, especially for our AI team. And Julie announced my promotion that no one else knows about, even in the company so, thank you. That stays here, please, in this room, until next all hands, on Wednesday. With that being said, a little bit of background about me. I’m Moroccan.

Lamya Alaoui: I moved to the US about 10 years ago. I will ask a lot of grace because my brain is wired in French. Sometimes there will be French words that will come out from my mouth so please be graceful about it if you can. With that being said, this is one of my favorite things because as a Moroccan who’s half Muslim, half Jewish, went to Catholic school, I thought I got it covered, but I was the lady the first time I landed in Germany because I was not aware that you’re not supposed to kiss people to greet them. But this is one of my favorite pictures to show and talks or even in workshops, because I’m pretty sure it happened to all of us one way or another. We show up and we think that we got it right. It’s what we’re familiar with and actually it’s not what the other person is expected.

Lamya Alaoui: Throughout my career, I have built teams in very, very different countries, different cultures. At Hala we have over 17 nationalities. Altogether we speak 25 languages. It makes it interesting for the meetings, believe me, where you have side conversational Slack in a whole different language, but you still need to deliver officially in English. The translation is super weird sometimes, but those are anecdotes for another time. That leads me to one of the lessons that I learned very early on. It seems obvious that when you’re building teams and you want them to perform, one might assume you want to know your mission and your values, but it doesn’t happen as often as one might think. The mission is basically your GPS. You want people to rally behind a common goal.

Lamya Alaoui: You want them to believe in that and it’s also what’s in it for them when they’re working for a mission. That means that it needs to be aligned with their own values or their belief system. SVlues start very early on. We all have seen or worked for companies where the values are stated. How many of you when you joined the company saw values listed on the website or have been talked to? How many of you were explained to what are the associated behaviors and expectations when it comes to those values? Oh, we have less people. This is one of the first lessons that I always recommend to people to kind of follow. I learned the hard way, by the way, is that know your mission, state your values. Values should come from the leadership of course, but also account for it in the hiring process, which we will get a little bit into it, but be very clear about what are your expectations are when it comes to values.

Lamya Alaoui: If I’m thinking about transparency, for me, it means having information that I need to do my job, but some other people, they think that they need transparency. They need to have access to everything. We all have someone like that in our companies. Don’t we? Setting those clear expectations and associated behaviors are very important. And mission is like literally your North Star. This is why it’s really important. The second one is seek first to understand. This is from Stephen Covey. There is a second part to it which is, then to be understood. When you are building diverse teams, we all come from different backgrounds, we have different understandings, often we speak different languages. We can also come from different cultures, high context or low context and if we don’t have mutual understanding we cannot succeed because then everyone is convinced that they’re right, that their approach is again the one that everyone needs to follow, but that kind of hinders teamwork, which is actually essential to high performing teams.

Lamya Alaoui: Listen. We all think that we listen pretty well, but we actually don’t because as human beings we have been trained, for the last few centuries, to listen to answer. We’re almost never listening for the sake of just listening and there is amazing research about that, that I will be more than glad to share. In working in very diverse teams or diverse teams in general, listen, help, create understanding, respect, which is a very important when it comes to having high performance teams as well. How many of you would say that they listen really, really, really well. Okay. We have four people in the whole room. That gives you something to think about. That’s another, and I’m a good listener, but still a lot of work to do with that.

Lamya Alaoui: Acknowledge that you will face cultural differences. When you join a new team, we all want to get along. Again, its human nature. We want to belong somewhere. We’re super excited. It’s a new job. We just went through the hiring process. We landed the job and then you show up in that meeting or you’re meeting your team and sometimes you see people that you have nothing in common with, and you still try to ignore that, which is again, human nature. Beautiful thing. Just as a manager, as a team member, be prepared that yes, we will have some challenges. We’re not seeing things the same way. One of the best examples that I always give here is like, I’m from what would qualify as a high context culture, which is there is no explicit direct messaging.

Lamya Alaoui: Basically I can be in my office and say, hey, I have a lot of work and I’m hungry. My expectation is that my coworker will just get it and go get me some food. Now, imagine if I’m having someone from a low context culture. In their head, they’re like, I mean, you’re going to go get your food when you’re done. That creates a fracture that it’s not even intentional because there will be resentment from my end. Why didn’t he get it? Having those conversations upfront is quite helpful because then people know again, what to expect. Assess the degree of interactions. When teams are being built, most of the time what happens is no one is thinking about how often the team members will have to work together, at what intensity.

Lamya Alaoui: Before starting to build the teams or adding members to your teams, just make sure that, hey, how often do they have to have, I don’t know, sprints. If it’s once a month, eh, you might want to have people that are pretty much aligned having somehow the same thought process and we’ll get to that in a little bit. If they’re not interacting a lot, you have a lot more room to assemble your team. Always, there are three degrees of interactions. We have low interdependency, which is people are doing their things on their own and sometimes it’s like going in a [inaudible]. Medium, which is a hybrid and then high interdependency, which the output from someone becomes the input for someone else.

Lamya Alaoui: Therefore, things need to be structured in a very certain way in making sure that people get along and understand, again, what’s expected from them. Communicate, communicate, and communicate and when you think you are done, communicate some more. One of the things about communication is that we all think that it happened. Again, the question, oh, disclosure, I ask a lot of questions. I mean, I used to be a recruiter. It comes with the territory basically. How many of you thought in a meeting that you were crystal clear and then two days later you discover that you were not.

Lamya Alaoui: When it comes to communication in teams, again, thank you, Slack, everyone in meetings, or usually where everyone is doing a lot of things at the same time. You’re talking, you think that you got your point across. Always ask a question at the end. What do you think? What are your takeaways? Just to make sure that people are on the same page and teams, high performing one’s, one of the best practices is having action items assigned, even if it’s an informal meeting or if a conversation happened on Slack, on, I don’t know, any other platforms. Even sometimes now, oh, we have also Asana now people add us to Asana and they think that, hey, we’re good.

Lamya Alaoui: No, make sure that the person actually went in, is understanding what you’re waiting for or expecting. Diversity and leadership. Clearly icons still have a long way to go in terms of diversity, but again, high performance teams when it comes to diversity need to see that diversity reflected in the leadership and in the management. Research shows that usually entry level management, there are sometimes good numbers, but as soon as we go to director, VP and above underrepresented groups tend to become less visible or they’re non-existent and C-suites and VP, EVP, SVP type of roles.

Lamya Alaoui: It’s also very important that the leadership reflects the diversity that the company is striving for. Again, an obvious one, hire the right people. This one is very dear to my heart because as I said, I’m a recruiter. Again, we’re only humans. We tend to hire people who are like us, who think like us, who share our values. Anyone wants to venture what happens when you hire people who are like you and your team and you’re building a team of clones, basically from a cognitive perspective. Anyone? Yes.

Shika: You wouldn’t get to learn something different from what you already know.

Lamya Alaoui: Yes. Anyone? Thank you so much. What’s your name?

Shika: Shika.

Lamya Alaoui: Shika. Thank you, Shika for volunteering. When you build a team of clones and this is a tech talk after all, how do you think that you can innovate? How do you think you can perform if everyone is thinking the same way and not challenging the ideas that are being discussed? Chances are very, very, very slim.

Lamya Alaoui: When you’re hiring the right people for your teams you have to look at three things. The first one is the values, because that’s, again, what will be the foundation of the team that you’re building. The second one is what’s missing from your team. An ideal team has five type of people. You have a theorist, you have a strategist, you have someone who is an analyst, you have a manager and you have an implementer. When we hire our teams usually we hire in desperation because you know, you got to deliver on that project.

Lamya Alaoui: No one is thinking about the composition of the team in itself. In tech, my experience showed we have a lot of theorists and strategists and a lot of analysts, sometimes zero implementer and zero managers. Things don’t get done somehow. Always look at what’s missing and finally, the third part is people who are willing to learn, who have a curious mindset and are eager to grow, because this is how actually innovation happens. People who are not afraid basically of questioning the status quo.

Lamya Alaoui: The other lesson is constantly scan for misunderstandings and ways to clarify. Again, here I will go to the low context, high context type of thing so, let me give you an example. I think it might be easier to make my point that way and I will ask a question at the end. In high context cultures or in some cultures when you ask questions, no, is not an acceptable answer. You have to say yes and some others you have to say no three times before saying yes.

Lamya Alaoui: Imagine when you have people from different cultures in the same room and someone is asking a close question that requires a yes or a no. What are the chances, based on what I said, that you will get an accurate answer to your question? 50%, 60%, 20%, a hundred percent. For this always ask open ended questions to give people space to answer without having to break their own boundaries and to make sure that you are getting clarity in the answers. How many of you were in meetings where you felt that you were misunderstood when you were speaking? What did you do? Anyone wants to volunteer again? When you felt that you were misunderstood? Yes.

Angela: Have you summarized in a written notification? I gave action items to the people I wanted clarification from that helped with the misunderstandings.

Lamya Alaoui: Thank you, Angela. One of the things that is really helpful and again, best practice for high performing teams, always summarize and put things in writing and this setting English is the working language. We do have a lot of people, I know in my team, I have a lot of non-native speakers. I’m not a native speaker so to make sure that the message gets across and that it’s clear it’s always in writing, always, which we tend not to do because everyone is working long hours or you have back to back meetings. Then at the end of the day you just want to finally get to do your work and you never summarize in writing what happened in those meetings.

Lamya Alaoui: Finally, the structure. Build a structure that is around diverse teams. When we’re usually building teams we have, we’re looking at what now is referred to as visible diversity, which can be ethnicity, religion, gender and many other things, but we’re never looking at the cognitive one that also has a heavy part, especially in tech. Those are recommendation that I’ve always lived by every time that I join a new company. We do a reintroduction and relaunch of the core values. We redefine them. We create, and this is like a company work, it’s not just someone in their corner doing it, but it’s a collaborative work setting what are the core values, what are the expected behaviors and the ones associated with each one of the values, and then it’s communicated throughout the whole company. When it comes to performance review it needs to be designed with underrepresented communities in mind.

Lamya Alaoui: Performance reviews, if you look at the history of how it was designed, it was designed for a very specific community or population. It doesn’t fit other people. Just leave it at that. Work on creating a performance review management system that is more inclusive. Yeah. Targeted networking. Again, this ties to the diversity and leadership. One of the things, again, hiring in despair. When those roles are open, people tend to hire fast from networks that they know because they don’t have networks that are already established. This is something that is quite helpful. I don’t think there are any people in the HR realm tonight, but all of you can be ambassadors for something like this to be established in your companies. And again, clear expectations around the engagement and the roles. What are the pathways to promotions? Again, if you look at internal promotions or internal applications, woman tend for, this is just an example, women tend not to apply to internal openings when it comes to higher positions in the company.

Lamya Alaoui: Sometimes just because they don’t check all the boxes so the work starts before that, where we actually need to make sure that the job descriptions or the job openings are reflecting and are being thoughtful about underrepresented groups. Finally, this is one of my favorite things, Kaizen. There is always room for improvement. Performance and diversity are a very long journey. Each company is at a different stage or different phase. Just ask questions, be kind because not a lot of people think about it when it comes to those things and be as curious as possible because at the end of the day the only way to know other people is to ask questions. Thank you so much everyone.

Julie Choi: I want to thank all of our speakers tonight and for all of you, for being such an incredible audience. We have people who have been watching these talks outside in the overflow and I’m told that, I think it might be better if we just all go out and just mingle. Get some fresh air after all this time is for us to connect and network. Why don’t we continue the conversation outside? Thank you everybody.

mosaicml girl geek dinner ukranian original borsch tshirt

This site reliability engineer discusses Ukranian borscht or machine learning, or both, at MosaicML Girl Geek Dinner.

mosaicml girl geek dinner speakers tiffany williams banu nagasundaram laura florescu julie choi lamya alaoui shelby heinecke angela jiang angie chang amy zhang

MosaicML Girl Geek Dinner speakers after the event: Tiffany Williams, Banu Nagasundaram, Laura Florescu, Julie Choi, Lamya Alaoui, Shelby Heinecke, Angela Jiang, Angie Chang, and Amy Zhang.

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Girl Geek X Volunteers Attend Capstone Project Presentations With CCPA High School Seniors In Oakland – Day 2

senior capstones april

Girl Geek X’s new partnership with Oakland Public Education Fund in adopting Coliseum College Prep Academy (CCPA) provides valuable experiences for Girl Geek X community volunteers to engage with the local school community. Join the Girl Geek X email list to be notified of future events.

By Angie Chang

When I was in high school, I didn’t have any industry role models. Most students didn’t go to a four-year college. School projects are valuable learning experiences and invaluable project-management and time-management exercises. Team work is a foundational building block for future success.

CCPA high school students experienced the journey of an entrepreneur building their minimum viable product (ideating, running surveys and customer interviews to validate assumptions, performing competitive analysis, building wireframes, managing features, and launching apps with native app builder Thunkable) as their Senior Capstone Projects.

As high school students, they likely do not know tech jargon. This is where industry mentors from companies across the San Francisco Bay Area come in to share their affirmative experiences and say hey, you’re not too far off from doing what the “professionals” and “techies” do at work.

Today’s presentations of students apps in Portable 2 focused on climate change (student education), shelter needs (items most in need at shelters), and healthcare (providing resources for free or low cost services when possible to low income and/or non-English speaking communities).

Volunteers attended student presentations and reinforced educator feedback by asking questions of the students presentations. Where did the data come from? Have you considered this additional use case? Why did this require building a native mobile app, versus just using Facebook to achieve your stated objectives?

After the presentations, Girl Geek X volunteers introduced themselves to the students. Marilyn works in user experience with a background in computer science, Katie is a Data Analyst at Playstation, James is a project manager, etc.

I talked about the thousands of early-stage startups that are great entry points (versus the big tech brands) for people new to tech. Together, we painted a picture of a diverse slate of roles in tech – filled by people of various education backgrounds.

We are excited to wrap up our first year with our “adopted” school CCPA in East Oakland with the Oakland Education Fund partnership, and excited for the next year!

Girl Geek X Volunteers Attend Capstone Project Presentations With CCPA High School Seniors In Oakland – Day 1 of 2

senior capstones april

This guest blog post was written by Girl Geek X community volunteer Annie Chang.

Girl Geek X’s new partnership with Oakland Public Education Fund in adopting Coliseum College Prep Academy (CCPA) provides valuable experiences for Girl Geek X community volunteers to engage with the local school community. Join the Girl Geek X email list to be notified of future events.

Upon arriving at CCPA’s auditorium for a debriefing for Girl Geek X volunteers to learn about their roles in participating for Senior Capstone Presentation Day 1, we divided into groups for rounds of presentations, questions, and answers — as well as areas of growth and strength. CCPA instructors guided Girl Geek X volunteers to their classrooms for demos of senior projects.

For their capstone presentations, students were asked to identify a problem within their Oakland and local communities. Breaking out into small groups allowed students to experience teamwork, collaboration and participation. This is the culmination of three separate classes with educators.

senior capstones april journie ohjai

Kicking off presentations in Portable 2, students present “Journie” — their response to identifying the mental health issue of having writing therapy and access to counselors. After enthusiastic questions and answers, student and programmer Armando Figueroa was asked what was the greatest lesson learned after a year-long process of group work. He responded enthusiastically: “Time waits for nobody.” Resoundingly, the group agreed their pain points were time management and communication.

Next, M n As Team presented their app “Sobriety Companion” impressively. After presenting statistics about populations suffering from substance abuse disorder, they noted that participants leaving a rehab treatment program have a 40-60% chance of relapse.

senior capstones april sobriety companion features

The relapse-prevention app comprehensively offered a daily pledge, progress tracker ((counting sobriety time), learn from others page, and a map consisting of the nearest Alcoholics Anonymous / Narcotics Anonymous / Methamphetamine Anonymous self-help 12-step meetings as well as links to rehab treatment centers. When asked about areas of growth, students admitted to being prone to procrastination and hastily finishing tasks last-minute.

P.A.R.T.E. demoed their app “Mental Evolution” containing features such as a mood journal, map, calendar and community space (for sharing content based on mental wellness challenges). This group used Google Forms to create a survey of their communities and decided to focus their target demographic of low-income communities.

senior capstones april mental evolution

Girl Geek X volunteers got to see wireframes of early mockups of the app and after Q&As, students were asked what was the highlight of working on their capstone project. The ladies enthusiastically noted their affinity for group work, team work and collaboration, and cited their strong communication skills as well as alignment of values.

You can still register to volunteer for April 27, 2022 (4pm-6pm) at CCPA school in East Oakland with Girl Geek X to attend more senior student presentations!

Job Opportunities from Girl Geek X Trusted Partners

MOSAICML IS HIRING!

MosaicML creates software and platform technology that makes ML training more efficient through algorithmic and system level innovations that improve the way neural networks are trained.

NEW RELIC IS HIRING!

New Relic delivers the only unified data platform that empowers engineers to get all telemetry—metrics, events, logs, and traces—paired with powerful full stack analysis tools to help engineers do their best work with data, not opinions.


Take a look at these job opportunities from our sponsors and government participants:

👩🏾‍💻 Atlassian 👩🏻‍💻 Slack 👩🏽‍💻 Strava

Autodesk 👩🏾‍💻 Front 👩🏼‍💻 Intel 👩‍💻 Ironclad 👩🏻‍💻 MosaicML 👩🏾‍💻 Opendoor 👩🏾‍💻 RelationalAI 👩🏼‍💻 Splunk 👩🏿‍💻 US Digital Service
Fisher Investments 👩🏻‍💻 Meta

Atlassian is a leading provider of collaboration, development, and issue tracking software for teams. With over 100,000 global customers (including 85 of the Fortune 100), we’re advancing the power of collaboration with products including Jira, Jira Service Desk, Jira Ops, Confluence, Bitbucket, and Trello.

Check out open jobs at Atlassian!
Slack has transformed business communication. It’s the leading channel-based messaging platform, used by millions to align their teams, unify their systems, and drive their businesses forward. Slack is where work happens.

SLACK IS HIRING!

Check out open jobs at Slack!


Strava is Swedish for “strive,” which epitomizes our attitude and ambition: We’re a passionate and committed team, unified by a mission to build the most engaged community of athletes in the world. With billions of activity uploads globally, we have a humbling and audacious vision: to be the record of the world’s athletic activities and the technology that makes every effort count.

STRAVA IS HIRING!

Check out open jobs at Strava!


Autodesk is changing how the world is designed and made. Our technology spans countless industries, empowering innovators everywhere to solve challenges big and small. From greener buildings to smarter products to more mesmerizing blockbusters, Autodesk software helps our customers to design and make a better world for all.

AUTODESK IS HIRING!

Check out open jobs at Autodesk!


Front is your hub for all things customer communication. Behind every amazing business is, well, people: a team and customers. And no matter what industry you’re in or where you’re located, it’s those human-to-human interactions that make your experience with a business truly stand out.

FRONT IS HIRING!

Check out open jobs at Front!


Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges.

Check out open jobs at Intel!


Ironclad is the #1 contract lifecycle management platform for innovative companies. It’s the only platform flexible enough to handle every type of contract workflow, whether a sales agreement, an HR agreement or a complex NDA.

IRONCLAD IS HIRING!

Check out open jobs at Ironclad!


MosaicML is a deep learning startup with a mission to make machine learning training more efficient for everyone through fundamental innovations in algorithms, systems, and platforms. We believe that large scale training should be available beyond the well-resourced companies, and bridging the gap between research and industry is core to our success.

MOSAICML IS HIRING!

Check out open jobs at MosaicML!


Opendoor’s mission is to empower everyone with the freedom to move. We believe the traditional real estate process is broken and our goal is simple: build a seamless, end-to-end customer experience that makes buying and selling a home stress-free and instant through technology.

OPENDOOR IS HIRING!

Check out open jobs at Opendoor!


At RelationalAI, we offer a cloud-based Relational Knowledge Graph Management System (RKGMS). We believe relational knowledge graphs are the ideal foundation for data-centric systems – systems that learn, reason, and predict over richly interconnected data.

RELATIONALAI IS HIRING!

Check out open jobs at RelationalAI!


Splunk Inc. turns data into doing with the Data-to-Everything Platform. Our powerful platform and unique approach to data have empowered companies to improve service levels, reduce operations costs, mitigate risk, enhance DevOps collaboration and create new product and service offerings.

SPLUNK IS HIRING!

Check out open jobs at Splunk!


The United States Digital Service is a startup at The White House, using design and technology to deliver better services to the American people. We partner leading technologists with dedicated public servants to improve the usability and reliability of our government’s most important digital services. When you work at the U.S. Digital Service, you change the lives of millions of Americans.

Check out open jobs at US Digital Service!


Fisher Investments is a different kind of investment firm. We don’t come from Wall Street, nor do we believe we fit in with most of the finance industry, and we’re proud of that. We work for a bigger purpose: bettering the investment universe. As of 12/31/21, Fisher Investments and its affiliates have offices in 8 countries and manage over $208 billion in assets for more than 100,000 clients.

FISHER INVESTMENTS IS HIRING!

Check out open jobs at Fisher Investments!


Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality.

META IS HIRING!

Check out open jobs at Meta!

Watch The 15 Top-Rated Sessions from ELEVATE 2022 Conference!

20 Female Architects in Engineering to Watch!

This is an updated list of even more talented technical experts in engineering, data, systems, cloud, and more – In 2017, we created a popular list of 12 Female Architects in Software and Data! Below are many more inspiring architects to watch in 2022:

#1 – Aida El-Toumi Murphy – Cloud Architect – AT&T

Aida has over 25 years experience at AT&T, where began her career as a software engineer. She is currently Cloud Architect at AT&T in the New York area. She earned her BS in engineering from Cornell and MS from UC Berkeley College of Engineering. In her spare time, she’s volunteered with Toastmasters.

#2 – Allison Holloway – Architect – Oracle

Allison has over a decade of experience working at Oracle, and is active in the database research community. She is currently an Architect at Oracle. She earned her BS in EECS from The University of Texas at Austin, and PhD in CS from University of Wisconsin-Madison.

#3 – Avery Wong Hagleitner – Software Architect – IBM

With over 20 years of experience at IBM, Avery is currently a Software Architect at IBM. She earned her BS in computer science from UC San Diego and her MS in software engineering from San Jose State University. In her spare time, she is an avid traveler and hiker.

#4 – Bhakti Mehta – Chief Architect, Confluence Cloud – Atlassian

With over 20 years of experience architecting, designing, and implementing software solutions on top of Java EE and other related technologies, Bhakti is currently Chief Architect Confluence Cloud at Atlassian. She earned her BE in computer engineering from Ramrao Adik Institute of Technology, and her MS in computer science from Binghamton University. She is the author of Developing RESTful Services with JAX-RS 2.0, WebSockets, and JSON, Packt Publishing, and RESTful Java Patterns and Best Practices.

#5 – Bing Zhu – Software Architect – Cadence Design Systems

With over 20 years of experience at Cadence Design Systems, Bing Zhu is currently a Software Architect at Cadence Design Systems. She earned her PhD in computer science from Peking University. She holds numerous patents.

#6 – Divya Mahajan – Director of Architecture – Fidelity Investments

Divya has over a decade of experience in engineering at Fidelity Investments. She is currently a Director of Architecture at Fidelity Investments. She earned her BS in information science from Visvesvaraya Technological University, her MS in MIS from Worcester Polytechnic Institute, and completed coursework (Data 8.1x: Foundations of Data Science: Computational Thinking with Python) at UC Berkeley through edX. In her spare time, she likes to hike, and can found on the mountains of New Hampshire, Africa, or South America.

#7 – Iris Melendez – Data Architect – Autodesk

Iris has worked as a developer, implementer, business and systems analyst, data modeler, data warehouse designer, data modeler, consultant, web designer and developer, project and people manager, and any other hat you can think of since the early 1980s. With over a decade of experience in data architecture and data warehousing at Autodesk, Iris is currently a Data Architect at Autodesk. She earned her degree in interior design from San Francisco State University.

#8 – Jeanine Walters – Principal Architect, Software Engineering – Salesforce

With over 18 years of experience working at Salesforce, Jeanine is currently a Principal Architect in software engineering at Salesforce. Prior to Salesforce, she held multiple technical positions for companies great and small, including an internet company that she founded. She earned her BS in math with computer science from Massachusetts Institute of Technology. She enjoys dancing and playing Capoeira.

#9 – Katie Sylor-Miller – Frontend Architect – Etsy

Katie is currently a Frontend Architect at Etsy, with over six years of experience at Etsy. Prior to Etsy, she worked at ConstantContact and EF Education in Massachusetts. In her spare time, she co-authors the zine Oh shit, Git!, based on her eponymous website ohshitgit.com. She earned her bachelor’s in computer science from Harvard Extension School.

#10 – Kris Berg – Senior Software Architect – Autodesk

Kris is currently a Senior Software Architect at Autodesk, with over 24 years of experience working at Autodesk. She’s working on the Fusion 360 product, which allows customers to create mechanical 3D Designs and define the manufacturing process. She earned her BS in computer science from Oregon State University.

#11 – Leena Sampemane – Distinguished Architect – Intuit

Leena has over 25 years of experience working product and architecture at companies like Oracle and Intuit. Currently, she is a Distinguished Architect at Intuit, where she’s been for almost a decade. She earned her BS in general studies from Charter Oak State College and her data science credential from UC Berkeley, Haas School of Business.

#12 – Liping Dai – Lead System Architect – Visa

Liping has over 25 years of experience working in engineering. She is currently Lead System Architect at Visa, where she’s been at for over five years. She earned her BS in computer science from Tongji University and her MS in software engineering from San Jose State University.

#13 – Maria Lucena – Director of Architecture – Fidelity Investments

Maria is currently Director of Architecture at Fidelity Investments, where she’s been at for over six years. She has over a decade of experience working in software engineering. She earned her web development certificate from Strayer University, her Associate’s in IT from University of Massachusetts Lowell, and her BS in computer science from Tiffin University. She considers her two beautiful boys her most significant achievements.

#14 – Minnie Ho – Architect – Zoox (Amazon)

With over 20 years of experience working in engineering, Minnie is currently Architect at Zoox (acquired by Amazon), where she’s been for almost two years. She spent most of her career at Intel as a chip architect. She earned her BS in EECS from Princeton University, her MS and PhD in EECS from Stanford University, and deep learning and self-driving cars course certificates from Coursera. In her spare time, she’s been a board member of the Palo Alto Chamber Orchestra.

#15 – Mónica Carrillo Goren – Staff Engineer, Platform Technical Architecture – Slack

Mónica has over 20 decades of experience working in engineering and leadership working at companies including The Honest Company, Facebook, MySpace, Verizon, and Lucent Technologies. Currently, she is a Staff Engineer in Platform Technical Architecture at Slack, where she’s been at for over five years. She earned her BS in computer science and engineering from Ohio State University.

#16 – Natasha Gupta – Software Engineering Architect – Salesforce

With over 15 years of experience working in engineering at companies like ExactTarget and Salesforce, Natasha is currently a Software Engineering Architect at Salesforce in Colorado. She earned her BE in electronics engineering from The Maharaja Sayajirao University of Baroda and her her MS in EECS from Indiana University–Purdue University Indianapolis. She’s volunteered at SQL Saturdays in New York and speaks at women in tech events.

#17 – Snezana Sahter – Distinguished Architect – Intuit

Snezana has over 25 years of experience working in engineering architecture. She is currently a Distinguished Architect at Intuit, where she’s been at for over four years. Prior to Intuit, she was a principal architect at eBay for over a decade. Originally from Serbia, she has spent most of her engineering career in the San Francisco Bay Area.

#18 – Sudeshna Biswas – Lead Data Architect – Visa

Sudeshna has over 20 years of experience working in large scale distributed data warehouse & business intelligence solutions. Currently she is a Lead Data Architect at Visa, where she’s been at for over three years. Prior to Visa, she co-founded Stadea Tools and worked at SurveyMonkey, Intuit, eBay, Cisco, and Apple. She earned her BE in Engineering from Jadavpur University.

#19 – Tong Qin – Software Architect – Autodesk

With over 15 years of experience working in software engineering, Tong is currently a Software Architect at Autodesk.

#20 – Ümit Yalçınalp – Architect – Oracle

Ümit has 25+ years of experience in the industry pioneering products and initiatives for the Cloud, Web, SOA and Java Technologies. She is the co-author of a book, several patents, an editor and contributor to various standards in Web Services, Java, XML and SOA. She is also a co-founder of Turkish Women in Computing community. She earned her PhD in computer science from Case Western Reserve University.

Cadence Girl Geek Dinner – Lightning Talks & Panel! (Video + Transcript)

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Transcript of Cadence Girl Geek Dinner – Lightning Talks & Panel:

Angie Chang: Hi. Welcome everyone to the Cadence Girl Geek X event. My name is Angie Chang, and I am the founder of Girl Geek X. By means of introductions, I mean to say my name’s Angie Chang and I’m the founder of Girl Geek X and Women 2.0. I also spent some time working at a company called Hackbright Academy, which is a women’s coding bootcamp. I also spend a lot of time talking about women starting high-growth, high-tech companies, working in tech, blogging about it. Sometimes I write listicles of women architects, women CTOs, VPs, security chiefs and such.

Angie Chang: Let’s see. Why don’t we do something? Why don’t we pretend that we have our “Hello my name is” name badges, and write in the chat like, “Hello, my name is Angie Chang, Founder, Girl Geek X,” and then put in your LinkedIn URL. I’ll start. I’ll copy and paste this into the chat. That way… I’ve noticed that people at our Zoom events have been sharing their LinkedIn profiles. I want to be the first person to say that, yes, let’s definitely share LinkedIn profiles and share a bit about ourselves more than we can see on these Zoom meetings or Zoom webinars.

Angie Chang: Let’s see. A bit about Girl Geek X. We’ve been doing Girl Geek Dinners since 2008. We started with events at Google and Facebook when they were smaller companies in 2008, and then we went to all these different tech companies. We went to biotech companies. We went to a bunch of companies I’d never heard about before.

Angie Chang: But the thing about that is once I was there, I would learn so much about that company. I’d learn about the industry. I’d learn about the women that worked in it. I would see their job titles and I would be very inspired and educated at that point to recommend that company say to friends. Also, it was really great for networking.

Angie Chang: Hopefully if you have time, you can hang out tonight. Later at seven or so, we’re going to start the networking and Zoom breakout rooms where you can actually chat with each other and connect more in person. But if you can’t, it’s okay, this talk is recorded. All of our events are recorded and put on YouTube later, and that URL is youtube.com/girlgeekx.

Angie Chang: What else? If you want to look at all the events that we have hosted in the past, they’re all on our website. It’s at girlgeek.io/attend, and you can find all our previous events. For example, we were at Discord a week or two ago.

Angie Chang: We just wrapped our annual Elevate conference, which is something we do every year for International Women’s Day.We have an all-day event celebrating women and having a bunch of exciting women leaders speaking about topics like mental health and leadership, not just very ambiguous things. We literally had a keynote on decision making from a VP of engineering.

Angie Chang: We always have a call for submissions. People can in the fall apply to speak at that conference. About 10 of those people who applied to become speakers became speakers at Elevate. There’s definitely a chance that if you submit something, you can be selected to speak. You can also sponsor a Girl Geek event like the Cadence event, where you have an opportunity to put your women on stage and give new tech talks followed by a panel. Then we have some networking.

Angie Chang: There’s just so many companies out there. That’s I think a great opportunity to get out there in front of a bunch of eyeballs and create some great talks that we then put on YouTube. What else? Oh yes. We have a Q&A. If you have a question throughout the course of the event tonight, feel free to put it in the Q&A or you can ask it in the chat, but there is a Q&A feature, so feel free to use it. Some of our speakers may want to answer questions, but they may not have time to answer them on screen so they can pop into the chat and answer them later if you ask them. I want to do my first introduction.

Angie Chang: Alinka is the chief legal officer and corporate secretary at Cadence. She’s responsible for Cadence worldwide legal operations. She has served semiconductor and software companies and her entire in-house legal career at a lot of companies that you may have heard of. Before moving in house, she was in private practice for a decade litigating chemical product liability matters. Welcome Alinka.

angie chang girl geek x alinka flaminia cadence
Cadence Chief Legal Officer Alinka Flaninia welcomes audience to Cadence Girl Geek Dinner 2022.

Alinka Flaminia: Thanks Angie. Thanks for the introduction. On behalf of Cadence, we are so honored to partner with Girl Geek X to host this conference in celebration of Women’s History Month. Women have played a significant role in Cadence’s 34-year history, and I’m thrilled to share with you some of our efforts to create a more inclusive and equitable workplace for women and underrepresented groups in STEM. But before I describe some of our DEI efforts at Cadence, let me first tell you a little bit about the company, for those of you who are not familiar with us.

Alinka Flaminia: Cadence is a pivotal leader in electronic design, building upon more than 30 of computational software expertise. Manifesting our intelligence system design strategy, Cadence delivers world-class software, hardware, IP across all aspects of the design electronic systems. Our customers include the world’s leading companies, delivering extraordinary products from chips to board to complete systems for the dynamic market applications, including cloud and hyperscale computing, 5G communications, automotive, mobile, aerospace, consumer industrial and healthcare. It’s fantastic to work at a company where the same set of tools enables innovation across such a diverse set of industries.

Alinka Flaminia: Actually for me, it’s kind of mind blowing, and I believe that the true enabler behind Cadence’s success is our high-performance inclusive culture. Our one Cadence, one team spirit is core to who we are, and embracing diversity and fostering inclusion are key tenets of our Cadence culture. Cadence encourages and fosters diversity equity and inclusion on many fronts, internal and external, through recruiting and university partnerships, education, leadership training, pay equity and promotion and building community.

Alinka Flaminia: A few examples include our sponsorship, diversity and technology scholarship programs for women, black students and Latinx student to support these underrepresented groups in their pursuit of STEM education. We celebrate and support our employee-led inclusion groups for black, LatinX, veteran, LGBTQ+ and women employees and their allies to build community at Cadence and beyond. Cadence offers professional development through advanced leadership and mentorship programs specifically geared toward our girl geeks and black and Latinx employees.

Alinka Flaminia: Cadence is investing in the pipeline of a more diverse employee population through partnerships with nonprofits and organizations that serve underrepresented groups in STEM like the National GEM Consortium, Society of Hispanic Professional Engineers, National Society of Black Engineers, Society of Women Engineers, Out In Tech, Girls Who Code, and I could go on.

Alinka Flaminia: Our culture has been recognized globally, earning Great Places To Work awards in 14 countries around the world, including seven years in a row on Fortune’s 100 Best Companies To Work For, rankings on Europe and Asia’s Best Workplaces, listed on Newsweek’s Most Love Workplaces and the Best Place To Work For LGBTQ+ Equality on the Human Rights Campaign’s 2022 Corporate Equality Index. Diversity, equity, and inclusion are top priorities for me and the rest of the executive management team, and our board of directors.

Alinka Flaminia: We are excited to work with Girl Geek and highlight in this conference some of our amazing innovators at Cadence and hear how they are helping us solve technology’s toughest challenges. I am not a technologist, but I am most definitely a geek. Regardless of your specific interests, we girl geeks are united by our passion, our drive, and most especially by our curiosity. The speakers and panelists are some of Cadence’s very best girl geeks, I’m certain they’ll stoke your curiosity about our business and provide great tips for advancing your career. Thank you for joining us today, and I’ll now turn it back over to Angie to introduce our first speaker.

Angie Chang: Thank you, Alinka for that warm welcome from Cadence. Our first speaker tonight is Helen Zhan. She graduated from the University of Tianjin in 2000 with a double major in computer science and economics. She began her career as an IP/SOC engineer at NEC in Japan focusing on both design and verification. Shortly after that, she joined Cadence, where she’s been for the last decade plus. Her passions for debugging failures and finding the root cause of issues has allowed her to grow her career at Cadence. Welcome Helen, who is joining us from Beijing tonight.

helen zhan cadence growth engineering beyond metrics
“Growth Engineering Beyond Metrics” by Helen Zhang, Cadence Design Engineering Group Director.

Helen Zhan: Thank you everyone. I’m Helen from Cadence. Today, I would like to share my story along with the team girls. Okay. I will use them putting in my later talk. I will use the integrated IC, IP, IPG, DDR, LPDDR, PHY and Mbps in my later talk.

Helen Zhan: Firstly, I would like to introduce the function group of my team. Similar to other any centers, we do have the six different function groups along with our journey like the different kind of the design, verification, solution team and the success team. With all this different function team, it provide the complete system solution for the memory system of each customers.

Helen Zhan: Our team did start from the 2011. Then we have the big growth from that year. After three years, we did break into top four of the senior IP core ranking and along with that long journey, we did achieve many milestones of the word first silicon to ensure our IP become more challenging to the market.

Helen Zhan: Today, I would like to share with the story of the team grows. When we start the team, we need to find the proper goal of our team. So we had we will be the market co-developer of the team start. We did have some big investigation of the market. We find there are two type of the current IP company. One is like the flea market, which you could find everything you want, but that may not give you repeatable supply and with good quality. Another is like the mega store. You could find the product with a good quality, but it may not satisfy all your requirement.

Helen Zhan: Where should we go? We leave this question to the marketing investigation. Then we find in cloud market, we do have the different application like the mobile application, consumer application, cloud application and also automotive application. Often shows the market orientation thing. We figure out our target is to build a different style of the IP vendor and supplier to make a customized configurable solution with a good quality to fulfill the different customer request, satisfy all the products.

Helen Zhan: With this clear goal, then we will build a good team and to make the team improvement to satisfy the market requirement. As I started from the beginning, we did have six different function groups. Today, I will check the digital design group as an example for the team roles. When the team grows, we need to divide the team into different separations to have the different function focus. With that kind of, we could have the expert in each field to make our product become more productive and have the leading age technology.

Helen Zhan: With this subdivision, we need to have the clear ownership of each different field, and we do not want to limit any engineer in that field only. We would also like to expand his focus in different area. We want the clear ownership of the each field. In the meantime, we also want the mixed function focus of the different area. With this kind of definition, we could increase the team skill and we could also back up each other and improve together. This makes the team become better and more productive and efficient with a single one.

Helen Zhan: In the meantime, with a complex of the IC product, we have the clear boundary of the each function group, but we want that boundary to have the team have its own focus. We do not want that boundary become any barrier of the cross-function group communication. we would like this boundary become multi care. That will stress our boundary to avoid any backslide and any part we are missing in the development that to avoid any for other issue and surprise in the later production.

Helen Zhan: With all this strategy and the teamwork, we did build a fully verified DDR subsystem. As you can see in this picture, we could support the different configuration product, and it could have the customized features, which could satisfy the different market requirement like the mobile, automotive, cloud and consumer. That also help us that our DDR product in the leading edge to have won more customer today

Helen Zhan: With this 11 years journey, our team is already increased to a big size. How do the junior engineer now become the senior engineer and the senior becomes an expert and with the supervisor like the leadership? But how to help this big team become more stable and more productive. We considers the two area. One is from the technic side. We want the team always stand at the leading edge of the product. That allow us to always to work with a new protocol and to achieve the highest speed in the world.

Helen Zhan: When we start a product, when we define the protocol, that means there are many unclear area which won’t be exist when we start the product that need us to have the flexibility design to accommodate any new requirement coming later. Also, we need to use our experience to doing the predict and analyze for the orientation to avoid we are going to operate position to the market. Also the high speed is everyone is chasing today. We need to build the high speed architecture to satisfy the design requirement.

Helen Zhan: With this technical innovations, that allow our expert to have their own focus that ensure they always have the interest of this product in their career path. In the meantime, to leverage different Cadence, we could always use the latest methodology and advanced technique that help us to use a new design methodology in that every field and everywhere along with our IP development. Also, we adopted advanced flow and tool in our IP quality check to ensure we have the product with good qualities.

Helen Zhan: The silicon proven is another advantage here because with more and more high-speed requirement, the silicon proven as a big fact for the customers who want the IP supplier to provide. In the meantime with a big team, we also want to improve work efficiency. When we start any new product or any new feature development, we will need to avoid any one-off development to make our effort could be reusable or repeatable in the later product. In the meantime, we also need a comprehensive quality system to have the issue being detected earlier, to send a alert to the design team or other development team to avoid any later surprise in the customer products

Helen Zhan: With all this QC and the strategies we development the automation flow that could help us to release the manual resource to focus on the technical side. In the meantime, it’ll also reduce the effort and error with the manual operation. With all this strategy and technical, I believe that communication is the most important to have all this done. First, I would have to introduce these three word, look listen and learn. We need to look what the team’s working like, what the customer require and what’s the marketing requirement.

Helen Zhan: We also need to listen to the voice from everywhere that will have the leader to clear the request and clears the issues in our team and in the market and brand customer. With all these facts, we could learn what we want to do in the next space and in the next step. Also, we could find what we should do to solve the current to and improve the team. Then I believe the different type of the talk is also very important. That is not the leader to talk to the team member. It also want team members to talk his mentor, his mentor, and his leader to share are the different ideas.

Helen Zhan: I believe everyone did have its own thought on his work. Then we need a clear communication between each channel to help the team members understand the requirement and purpose and the goals of the leader and the management team and also help the leader to understand the consent or problems in the team member side to help them to solve that. With all this good communication and consideration, we will make this become execution that is to help us to make our goals and consideration become true. Thank you.

Angie Chang: Thank you, Helen. Our next speaker is Elena. She is currently the Global Public Relations and Social Media Director at Cadence. Previously, she had held communications and marketing roles at AgilOne, Coupa Software, SugarCRM and more. She spent over five years freelancing and consulting to communications and marketing. Welcome to Elena.

elena annuzzi cadence finding your growth career path

“Finding Your Growth Path” by Elena Annuzzi, Cadence Global Public Relations and Social Media Director.

Elena Annuzzi: There we go. All right. Thank you, Angie, for the intro. Hello, everyone. Welcome. As Angie said, my name is Elena, Elena Annuzzi. I’m the Global PR and Social Media Director at Cadence. My presentation tonight is going to be focused on Finding Your Growth Path.

Elena Annuzzi: How many of you have ever felt stuck in your career and you’re trying to figure out how you might be able to move it along? I think a lot of times it’s seen that promotions are an obvious way to move yourself along in your career, but there are also a lot of other things that you can do to propel your career and take it to the next step. I’m going to talk to you tonight about my own personal growth journey and also impart some tips that you can leverage to find your own unique growth path.

Elena Annuzzi: With that, I will start with my own personal growth journey. I’ve been in technical communications positions for the last 22 years. A lot of my career has been spent handling public relations, but I’ve also spent a lot of time doing analyst relations programs, content marketing, social media marketing in customer marketing programs. When I started, my role was strictly doing PR and I worked in house in a corporate environment.

Elena Annuzzi: A lot of times people in my career field do start in the PR agency realm, where they have access to lots of training and resources. I kind of missed out on that a little bit. I did find it a little bit difficult to be in a corporate position and kind of rise through the ranks in there, but I absolutely did best that I could to try to learn different facets of the business. Ultimately, I decided I wanted more growth, which led me to a path to consultancy. When I did that, I worked for a few Bay Area PR firms and also had clients of my own.

Elena Annuzzi: I definitely had my hands full for sure, but what that it is it kind of pushed me out of the comfort zone PR box that I started in. I got to dip my toes into other areas such as the ones that I mentioned, analyst relations, customer programs, things like that. It also imparted a lot of confidence in me as well because I had clients who were in all different industries, big or small. Oftentimes, if they were small, they might have been a one person marketing shop, so they were looking to me for leadership.

Elena Annuzzi: That really gave me the confidence to become the leaders that they needed and also acquire a much broader skillset than I ever anticipated. That was really a great period that I think then after five and a half years, I decided to reenter corporate. As I did that, I came in at that point as a very experienced person, leading teams and working on projects to get visibility for the firms that I worked in a lot of times from the ground up. They had never had PR before and they didn’t know what to do, so I kind of in there to build it back up.

Elena Annuzzi: Now, for the last seven years, I’ve been at Cadence. I started at Cadence as a senior manager in an individual contributor function. Now, I’m the director of the group, and I manage a team. The team is responsible for handling anything that is publicly distributed in the form of news releases, as well as contributed content. All the social media platforms are managed by our group and a variety of other things. We also work very closely with executive management. There’s lots of things that are sensitive or require them to do media interviews and things like that. Definitely something I really enjoy.

Elena Annuzzi: I’m glad that I had the opportunity to try lots of different things. I kind of take my consulting experience and I’ve sort of taken that along with me as I’ve gone along through my career. I try to always look kind of at the company from an outside view and try to establish, “Okay, well, if I was consulting, what would I recommend that this company do?” I’ve kind of had an interesting path and I’m currently very happy at Cadence and I have a great team, all amazing people.

Elena Annuzzi: Let me now continue with some tips. I will share some growth tips with you. The first one I have is make sure that you’re having open in conversations with your managers. If you haven’t discussed a growth plan already, make sure that you do that. If you haven’t really thought about it, maybe write some notes down before you have that conversation so that you then go into that conversation prepared.

Elena Annuzzi: Another thing I would say is to offer to take on new projects that are outside your comfort zone because then you’re sort of pushed to try something that you may not have otherwise done. You may experience a very pleasant surprise and that something worked out so fantastic for you that it would definitely be worthwhile to make the investment to try something different. Another tip would be to find groups who have similar interests to you, and that way you can gain new inspiration from others, as well as make some good connections.

Elena Annuzzi: There’s lots of ways to do that online today, for example, and you may be familiar already with social media groups in your related career field, so feel free to take a look at those. LinkedIn is probably the most obvious place, but other platforms have groups as well that relate to professional fields. The other thing too is if you have local meetups, check some of those out or even leverage your university, if they have alumni groups and more specifically alumni groups within your field of study.

Elena Annuzzi: Lastly, maybe volunteer with an organization that is also passionate about the things that you’re passionate about from a work perspective, like say you’re volunteering with a STEM group and you’re in a STEM field. You may meet some great connections that way and gain some new insights.

Elena Annuzzi: Continuing on, the next tip is be relevant. What I mean by that is making sure that you’re always kind of staying fresh and up to date on what the industry’s current best practices are. How might you go about doing that? You can have conversations with others, whether they’re peer groups in your company or people that you’ve worked with in the past who hold similar job functions and just kind of ask them how they’re approaching their job. Obviously certain things are proprietary, so there’s limits, but you can kind of get a good gauge as to how others are tackling a similar job to you.

Elena Annuzzi: The other thing that I’ll recommend, and this is not meant to sound intimidating to employers in the least… It’s actually for your benefit… is to check out job descriptions. The reason that I say that is you can take a look at job descriptions in a role that’s similar to yours and even look at those that are above your level because then you’ll quickly figure out what companies are demanding of people in those functions today. You can quickly realize, “Okay, I have these skills, but maybe I’m missing a couple,” so you can identify the gaps and then work to figure out how you can get that experience in your current role.

Elena Annuzzi: That would be something to talk to your manager about. If you’ve identified a gap, “Here’s something that I’m interested in trying, let’s do that.” Then in looking at the job of positions above yours, then you also have a gauge of what to shoot for kind of in your next step. Similarly, if you realize that you have some gaps, then you can work to address those. The next thing I would say is acquire new skills, taking new courses or attending conferences where you’ll have access to new information that you may not have otherwise had, or ask your employer, your HR department, or your manager about job sharing.

Elena Annuzzi: If you’re not familiar with that concept, it would be where you essentially do a job swap for a limited amount of time. Let’s say you have a peer organization and you want to take on some function of your peer because you have an interest there and want to explore that, you can maybe switch jobs for five hours a week and both of you are actually gaining a new skillset by doing that. The next thing I would recommend is mentorship. I would say find a mentor if you don’t don’t have one or be a mentor. Both things are absolutely critical.

Elena Annuzzi: I am so glad that over the past five years or so, I’ve seen a lot of mentorship programs kind of budding in the industry. That’s really a great thing to see. I kind of wish that I had those types of things when I was first starting my career. Cadence also does a really great job with this, by the way. We have an internal mentorship program where they match pairs up. It’s really just a phenomenal thing. If you’re not already in the realm of finding a mentor or being a mentor, I highly recommend that. The mentor for you can obviously serve as a sounding board. Whether the person’s in your industry or not, or maybe they’re your manager, maybe they’re someone who’s completely disconnected from your field altogether, it’s great to have somebody who can function as that sounding board for you.

Elena Annuzzi: Also being a mentor. It’s such a rewarding experience to pay it forward. I highly recommend that you try this and there may be some of you who currently aren’t managing a team, let’s say. If that’s the case for you, being a mentor, that will give you leadership experience. I highly recommend that next.

Elena Annuzzi: Next, here’s a few points to keep in mind. No two growth paths will look the same. Try not to compare yourself to others. The next thing I’ll say is always be curious. I always tell my team members the minute you’ve accepted the status quo, you’ve stopped growing in your career. Always keep that explorer hat on and try to figure out what you could be doing that’s different. The next thing I’ll share is ensure that those new areas that you decide to explore align with your organization’s business. If what you want to try aligns with the business, then it’s a much easier sell when trying to get buy in.

Elena Annuzzi: The next thing I’ll say is surround yourself with people who support you, whether it’s people inside your company, outside your company. It could be a mentor or just your team members, your manager, people in peer groups, make sure that you have great support all around you. Then the last thing I’ll say is have fun in the process. We all need to have some fun.

Elena Annuzzi: To conclude, I want to encourage all of you to start taking steps today to grow your career path. Those moves that you take today will start impacting your career now and well into the future. As a key takeaway, remember that it’s you who’s in the driver’s seat. Thank you very much for your time.

Angie Chang: Thank you, Elena. That was excellent. Our next speaker is Didem Turker. She’s a design engineering director in the IP group at Cadence, where she leads development of high-speed, high-performance communications circuits and systems. Before joining Cadence, she was the Senior Design Engineering Manager at Xilinx in the service technology group. She holds 11 US patents and authored numerous technical papers in the field of analog and mixed-signal circuit design. Dr. Turker has a PhD degree in electrical engineering from Texas A&M University. Welcome Didem.

didem turker melek cadence engineering director ip group effective technical presentations a powerful tool for your career success
“Effective Technical Presentations: A Powerful Tool for Your Career Success” by Didem Turker Melek, Cadence Engineering Director, IP Group.

Didem Turker Melek: Hello. Thank you, Angie. Okay, let me share my screen. Okay. All right. Okay. Thank you for this introduction, Angie. Hello, everyone. I’m Didem. Today, I’ll talk about effective technical presentations and how they have a key role in your and your team’s success. Before I begin, throughout my career, I found that being able to will communicate my work to my colleagues clearly had significant impact on the type of feedback that I got, but also on my work being recognized.

Didem Turker Melek: Over the years, this is something I championed in the teams that I worked with and we always saw really positive results. I’m hoping that this discussion will be helpful today for you too. Okay, let’s begin. When we talk about technical presentations, they are different than the general presentations that we may give to a wider audience.

Didem Turker Melek: We also need to share data and talk about more detailed material with certain technical complexity. Now, throughout our career, there’ll be different occasions where technical presentation may be called for. This could be academic conferences, customer presentations or when we are collaborating across different organizations in our company, it could even be within our own team if this would be to our close peers, our colleagues and maybe our management.

Didem Turker Melek: It is this last one that I want to highlight because this is a situation that we encounter really frequently, yet it’s also the one that we overlook the most. I really want to emphasize how important it is to communicate technical information through well prepared, clear presentations and especially around the audiences, people that you work with every day.

Didem Turker Melek: Even though the occasions and audience may be different, there are common goals when we are giving you technical presentation. The first one is effective information sharing. Being prepared with proper organized material will make a big difference over opening live results, showing live data or giving a verbal description. This is true even in a more informal team setting because for a discussion where we have technical complexity to discuss, the audience will have a hard time following if you’re doing it verbally.

Didem Turker Melek: The second goal would be to get feedback. You probably have bright people from different technical backgrounds and experience living in your audience, so use that brain power. The best way to get good feedback from them is by communicating your findings in a clear way. Third goal would be to train others so people can learn from your experience and maybe save some time.

Didem Turker Melek: Finally, it’s documenting our progress. The presentation material that you prepare will serve as good documentation of your work. It’ll help you look back in the future to track where you have been at a certain time. It’ll also help others in the future to look back and understand your work better. Depending on the situation, one of these goals may be more dominant than the others in your talk and you can prepare your material accordingly.

Didem Turker Melek: Okay, let’s talk about some presentation tips. First is know your audience. It’s important to know who the target audience is and their familiarity with the material. But here, what I want to emphasize is that they are not you. What I mean by this is when we spend so much time in the details of our work, we tend to forget that what’s obvious to us is probably not obvious to others. It’s important to keep this perspective in mind when preparing your material.

Didem Turker Melek: I think something that helps with this, and it’s a really good strategy overall, is to have a story. As you plan your slides, remember to build this story so you can bring your audience up to speed and along with you. Start by setting the big picture, why we started. This would be where you talk about the goal, the problem definition and big picture stuff. Next would be how we got here. If there were previous discussion or decisions that were taken, try to recap. Next is where we are now. This was the main discussion that you want to cover. Finally, where we go next. It’s always helpful to finish with next steps and a plan.

Didem Turker Melek: Now, another very important tip is use your voice and your point of view. I can’t emphasize this enough. When you are presenting your work, please remember that you are an expert and this is true even if the audience have people with more experience. You are the expert on your own data. What can we do? Each slide should have at least one key takeaway that you highlight. Please avoid doing a data dump and letting the data speak for itself. It’s really important that you make observations because that’s your contribution. You can use metrics to help people interpret the data, metrics such as target value specification, maybe margin to that spec and so on.

Didem Turker Melek: Finally, don’t be afraid to raise possible issues and don’t be afraid to ask questions. Let’s look at some examples. Here is a slide you may encounter in a technical presentation. Now, this is what I would call a data dump. This is a bunch of numbers and while it may be obvious to you, for someone who just saw this and has only minutes to digest, it’ll not be clear. What’s the takeaway here? Is there a target value and what are the units?

Didem Turker Melek: How can we make this better? First, notice that I removed some columns. When you have a large amount of data, it’s helpful to do a divide and conquer approach and present it in smaller, meaningful pieces. Now that I edit the target specification, this will help set the key numbers here in this table into context. I’m also using getting a visual help by making the most important column, which in this case is bandwidth mode and using color according to mark failures.

Didem Turker Melek: Finally, in the second bullet, I’m including my key takeaway and observation from this data, which is that we fail the spec at certain cases. Now, in addition to the key observation from the data that I just showed, I can also build up on it by adding more information. For example, I can explain why I think this failure happens and propose a mitigation plan. Now, the goal of this is to facilitate the right discussion. This is why you think the problem may be happening and this is how you think you may be able to solve it.

Didem Turker Melek: By sharing it this way, you can get the right feedback about your plan and maybe come up with a better plan as a team. Okay. I want to pause here and add a bonus tip. While I mostly focused on how you can help your audience better understand the data, there is one significant benefit of having slides like this with clear points. Let’s go back to this slide. You may have attended presentations where someone needs to share large volumes of data, maybe 50 to 100 slides. Every now and then, a slide like this will appear and they will go, “What was I going to talk about here?”

Didem Turker Melek: Now, it can happen to any of us? Instead, if you have a slide like this, now, even if you’re tired or anxious or nervous, or if you’ve just lost your train of thought, you have all the help you need in your own slides. You have the key point that you wanted to make, you have the discussion points to help you, and you have the visuals to make that up. By preparing slides like this, not only you’re helping the audience understand you better. You’re also helping yourself present it in a more clear and easy way.

Didem Turker Melek: Okay, Let’s go with another important tip. Drive the discussion. As the presenter, we are in the driver’s seat. It’s our responsibility to guide the attention of the audience to key points. Please remember that just because something is on a slide, doesn’t mean that the audience will notice it. You can use visual aids like the ones that I used in the previous slide, such as bold lettering, colors, boxes and circles. You can also use keywords such as issue, risk, meets, does not meet to grab the audience’s attention.

Didem Turker Melek: Okay, let’s look at another example. Here I am summarizing some results. This is basically a big block of text. There’s too much information packed in this one slide. It’s too busy and it’s not easy to digest. You may also notice that it’s inconsistent in the way it talks about results. I first see a number about some typical corner. Then I talk about something else meeting a spec. I throw in some comments about some simulation set up or environment and then I throw in more numbers and more setup related material.

Didem Turker Melek: Instead, what I can do is divide this into multiple pieces such as first setup and then the results and clear it up. But there is one more problem that I want to show. I don’t know how many of you here even noticed this, but there seems to be a major issue and it’s buried in a small bullet in the text. Something does not work. If we want to talk about an important issue or make sure that our audience knows about an issue that we observe, this is really not the best way. Now let’s try a different way.

Didem Turker Melek: First, notice that I use the keyword in the slide, issues observed. Now, this will definitely get the attention of the audience and I. There is no doubt that now this issue will be noticed. Next up, I state the issue itself. On top of that, I add some explanation and a possible resolution. I also included data in a graphical format. Now, whenever we highlight a key discussion point, it’s very helpful to have the data to back that up especially in a visual form like this.

Didem Turker Melek: I do want to note that when you include graphs, please remember to include axis titles because again, they may be obvious to you, but it may not be obvious to everyone and it makes it much clearer this way. Overall, when I present the issue like this, it’ll help me highlight and make sure that I get the right feedback and it’ll facilitate the right type of discussion

Didem Turker Melek: All right. Let’s recap with some key takeaways. First, well-prepare, technical presentations are powerful tools to help you communicate your work better, and you can utilize them in your weekly or regular technical meetings with your own team too. Two, if you’re presenting data, do it in a clear and organized way, so you’ll be accurately interpreted. A bonus tip here was that well organized slides will actually help you too when you’re presenting.

Didem Turker Melek: Third, for effective communication, use your point of view and guide the audience’s attention to where it needs to be. I’d also like to add that this is a skill like any other and practice will make it better. Start preparing those slides, everyone. Okay. Thank you. Thank you for your time.

Angie Chang: Thank you. That was excellent. Now, I’m going to bring up our panel and introduce to you our moderator for tonight. Jeannette Guinn leads the demand generation marketing organization at Cadence. Her experience includes a 20 plus year career in B2B tech marketing, owning a floral business and performing vocals of various cover bands across the Bay Area. She has volunteered as a Court Appointed Special Advocate, CASA, to foster children and currently serves on the Child Advocates of Silicon Valley board of directors. Welcome Jeannette.

rishu misri jeanette guinn dimitra papazoglou karna nisewaner cadence girl geek dinner
Clockwise from top left: Rishu Misri, Jeanette Guinn, Dimitra Papazoglou, Karna Nisewaner.

Jeannette Guinn: Hello, good to be here. I’m sorry. My audio cut out when you started the introduction. I’m assuming we’re going to kick this off. Hello everyone and welcome to the Cadence Panel on Women Empowerment. My name is Jeanette Zelaya Guinn, and I’m the Group Director for the Demand Gen Marketing Team here at Cadence. It is a true honor to be here today and it gives us a wonderful opportunity to have our voices be heard and valued. I’m joined here on the virtual stage by three amazing Cadence colleagues. To get this, this discussion going, I’d like to take a moment for each of them to do a quick introduction. Karna, let’s start with you.

Karna Nisewaner: Hi, my name is Karna Nisewaner, and I’m a vice president and deputy general counsel in the legal department here at Cadence. I started my career as an engineer, studying engineering at Princeton before moving to Singapore to teach basic electronics and seed programming at one of the polytechnics there before I pivoted my career over to law.

Karna Nisewaner: I’ve been honored really to be able to work for a number of different technical companies and for the last almost 11 years here at Cadence. I feel like my background in technology makes me a better lawyer for the company and allows me to really engage with all of the different teams and people here at Cadence. To me, that’s one of the best things about starting out your career studying technology is you have all these different options available to you, both as somebody that’s designing the IPs to somebody that’s marketing and telling people about stuff to somebody that’s helping on the backend with the legal patent protection, IP protection, or just basic contracts.

Karna Nisewaner: It’s just really so exciting to be part of what I think of as the future of the world, which is technology. For me, it’s great to be at Cadence, a place that’s really helping all these companies out there build the future. I’m just so excited to see where things can go. That’s why I really love my job and my company.

Jeannette Guinn: Awesome. Thank you so much, Karna. Thank you for being here. Rishu, let’s go to you.

Rishu Misri: Thanks Jeanette. Hi, I am Rishu Misri Jaggi. I work with Cadence as a senior principal technical communications engineer, but that’s a very long title. Doesn’t mean that I do the most important job at Cadence, but what it does mean is that I work with an organization that is at the center of technology, that I work with a male-dominated workforce.

Rishu Misri: Being a woman and a mother working at Cadence, what it means is that I get to maintain a very good work-life balance. I get to spend a lot of time with my kids whenever needed. I can attend to the parent-teacher meetings. At the same time, I can also be at the [inaudible] working and supporting on technology advancements with my other male counterparts. I can volunteer for various Cadence-sponsored community outreach programs that are focused towards empowering other women, kids and students.

Jeannette Guinn: Wonderful. Thank you so much, Rishi. To close it out with Dimitra on your introduction.

Dimitra Papazoglou: Okay. Hi everyone. My name is Dimitra Papazoglou, and I’m an application engineer at Cadence. I support the analog and mixed signal front of Cadence tools. My base is in UK, so it’s a bit late for me, almost 2:00 AM. At the same time, I need to watch my daughter. She’s 12 months old. She’s sleeping, so that’s good. That’s good. We can go and continue.

Dimitra Papazoglou: I’ve been working with Cadence nine years. I joined Cadence straight after university. I can say that I built my career at Cadence. I want to share with you my experience so far. When I started, I realized very quickly how challenging it is to work in this male-dominated industry. I still remember my first visit when I visited customer site and there were 10, 15 men, very experienced, and I was on the other hand very young and with no experience.

Dimitra Papazoglou: Since then, I had been trying to find answers to questions like how should I… What is the right position to stand? How should I use my voice? How can I look confident? In the end, I found all these answers to these questions, and then the support that I needed through a women community that was built internally at Cadence. I had the chance to meet and listen to the stories of several women and quickly realized that these are the women that really inspired me, my female colleagues.

Dimitra Papazoglou: Through them and through their stories, I got also inspired how to get promoted to the next level, how to face my return back to work this January when I came back from maternity leave. I’m really happy to have my female colleagues and those are the ones that really have inspired me and motivated to continue and navigate my career.

Jeannette Guinn: Wonderful. Thank you so much, Dimitra. I wanted to kick off the conversation with talking about current advocacy and what each of us do to empower women and underrepresented groups and why you do it. Why is it important to you?

Jeannette Guinn: I’ll kick it off. I recently became involved with a couple programs that were important to me. In my intro, as Angie stated, I am a board member for the Child Advocates of Silicon Valley program. It’s a nonprofit organization that provides court-appointed advocates for neglected and abused children. I was a former CASA volunteer. If you don’t know what that is, either reach out to me or look it up. It’s amazing. I did that for about five years and it changed my life and it made me realize how badly I wanted to become a mother. That’s where I started off my volunteer work.

Jeannette Guinn: I currently lead the Latinx inclusion group here at Cadence. It’s an opportunity to provide education on the Latin community. I’ve learned a lot and we’re interacting and learning a lot from the other DE&I groups at the company, which is just fascinating. Also, a committee member for the women and tech organization here at Cadence.

Jeannette Guinn: Then in my spare time, I just joined my local Little League board. I have two little girls, six and eight years old, Mia and Zoe. I often call it the Mimi’s and Zozo’s show because that’s pretty much my life. They’re both avid softball players. This was the second year that the league decided to do both baseball and softball under one organization. I saw the lack of softball visibility, and the girls were definitely treated differently. Wasn’t going to sit back and watch. I joined the board and with another female board member, we elevated the softball side significantly.

Jeannette Guinn: Yes, I use my very loud voice when I coach Mia and Zoe’s green Yoda’s softball team. Yes, very involved in that organization. Why do I do all of it besides trying to go crazy? I found myself just constantly complaining about things that were happening around me, and I didn’t want to sit back and watch. I wanted to make a difference and I wanted to make a change. I also want to be an example to my girls. I’m proving that we can make an impact in this world. That’s why I do it. What about you, Rishu?

Rishu Misri: Well, yes, I think I started with saying that I do get a lot of opportunity at Cadence to volunteer for various community outreach programs. I’ve been a member of the Make A Child Smile Society. We do anything that can bring a smile to a child, organizing fundraising events to sponsor the education or painting their schools or looking after their healthcare, taking them out for health checkups, even emotional care. We could take kids out for a day trip if needed, whatever that can make them feel a little better.

Rishu Misri: I’ve also been a member of the FMA committee at Cadence, which works towards female welfare. Under this program, we partner with an NGO in India called Goonj. We sponsor and one of the initiatives which focuses on welfare. The initiative is called Not Just A Piece Of Cloth and it focuses on increasing the importance in awareness around menstrual hygiene. There’s a taboo around talk about it, so we’re trying to break that taboo. Also raise funds that can go into providing for safe supplies for women and underprivileged sections.

Rishu Misri: More recently, I’ve also been volunteering for the Cadence scholarship program. Here we interact with military students from underprivileged societies. These are kids who are very bright, very enthusiastic, clear about their vision. A lot of them want to get into STEM careers, and the Cadence scholarship helps fund their academic goals. As mentors, we try to give them support with confidence building, time management, communication skills, and sometimes just act as sounding boards because the kind of issues they face with their academic sites, they may not have anybody at home to give them the ear. We sort of just support them there.

Rishu Misri: Those are all the kind of things. Sometimes also go and volunteer outside at my personal level. That’s really all the kind of things that I’m doing. Talking about why it’s important to help empower somebody, every time I come back from these events or an interaction like this, I may want to say that I have empowered somebody, but I think what I hear is I am empowered. It brings a lot more energy back into me when I come back from an event like this. It is not just the beneficiaries’ win. It is my win as well. It strengthens me a lot. That’s why it’s important.

Jeannette Guinn: Awesome. Dimitra, what about you?

Dimitra Papazoglou: For me, some years ago I’ve been asked and I’ve been honored actually to build and lead an internal women community at Cadence. I had the great chance to travel and meet in person more than 50 women from Cadence in Europe and Middle East. I had a great chance to talk to them and listen to their stories, understand their needs, and also the challenges that they face working in this environment, in this industry.

Dimitra Papazoglou: We as community team, we wanted to listen first to women and then set the objectives and find the best ways to empower them. What we have done is a set of actions, events. I’m going to mention some of them that I think that they can be also beneficial to everyone here, for the audience. Very beneficial is the talks given by women. The woman can be from outside or inside the Cadence organization. It can be from any level, from senior level or from an early career woman.

Dimitra Papazoglou: I do believe that everyone… You can always learn from a woman, no matter the level that she is. I can tell you an example. Karna, she’s also part of the panel. She actually gave an inspiring talk to the women of our community. She talked about her story, her career, the obstacles that she faced and how she overcame these obstacles. As you see that listening to this woman, you actually get the strength and the confidence on how to navigate and achieve your career and achieve your goals.

Dimitra Papazoglou: Another thing is what we do very interesting is regular meetings where we talk about topics like leadership, work-life balance. We talk about the talents that those topics have, and we try to find solutions together. Again, we talk to each other and try to help each other through these regular meetings. Another important thing is the trainings. We have done career trainings, but also body language trainings. I totally recommend this one. It’s one of the best trainings that I have ever done.

Dimitra Papazoglou: It is all about position, the right position to stand in, how to do the best use of your voice. I think many, many people have these issues like how should I talk? How should I present? I totally recommend these kind of trainings. They definitely can help you to strengthen your confidence. Why I think the women community is very important? Because through the networking that offers you and also the set of actions and events that I mentioned some of them, you can find through a community the mentors. You can find the role models. You can find the sponsors.

Dimitra Papazoglou: You can find all the answers about how to navigate your career and how to go to the next level. It can certainly contribute on how to achieve your career goals. I think it’s one of the best way for all the women.

Jeannette Guinn: Awesome. I just have to say a side note, the fact that you’re able to complete sentences at 2:00 AM in the morning is just impressive within itself.

Dimitra Papazoglou: And having a 12 month daughter, right?

Jeannette Guinn: Huge praises to you and onto Karna, your thoughts.

Karna Nisewaner: I feel like one of the things that I get the most joy from and that really helps benefit the community is the mentoring that I do for people, both internal to Cadence and external. You don’t have to be in the same subject matter as someone to help be that person that bounces ideas off of. As Elena mentioned earlier, it’s important to go to your manager with a plan or ideas to be that person that helps people come up with those plans or ideas and helps them review things ahead of time.

Karna Nisewaner: I feel like the internal mentoring I do within Cadence, particularly during the pandemic… I think it’s been important to help people as they’re just dealing with a lot of different issues and to be that sounding board. I feel like the more I progress in my career, the more important it is for me to reach out and be there for people.

Karna Nisewaner: Now, in the past, one of the things I loved doing was traveling. I think three or four years ago for International Women’s Day, I did a talk at one of our India sites. I went to all of our India sites and did talks to the women’s groups there. I loved being able to reach out to Dimitra’s group and do a talk right before she left on maternity leave. I thought that was great.

Karna Nisewaner: For me, it’s that ability to reach out and connect with people internally and externally and help be that sounding board that helps them move forward. To me, that’s how you, as an individual, can help others. You don’t have to be more senior. You don’t have to be in the same area, but you can be that really good sounding board and person who can walk through the ideas with somebody or can brainstorm things to think about. In the greater community, one of the things that I’m passionate about is making sure that women are able to work.

Karna Nisewaner: One of the things that really makes it difficult is effective childcare and during the course of the pandemic was also having school, which is a place where many of us have our kids and that allows us to have time at home to work. I’m on the board of a childcare organization in my community that runs the afterschool program and several infant and preschool programs because if you don’t have a place for your children to go, the people that tend to stay home are the moms, not the dads. I just think it’s important that we don’t cut people out of the workforce because they don’t have the support necessary to be able to go into work.

Karna Nisewaner: Then I think it’s also important to support your local school. I’m on a school psych council and help planning to create those environments where achievement gaps are addressed in kindergarten, where you’re looking at why is one group behind in reading, behind in math and behind in writing. What can we do starting in kindergarten, first grade, second grade to really stop the achievement gap there, build the confidence of everyone there, so that by the time they hit middle school and high school, everyone’s excited to learn? Everyone has that same background and the necessary ground level education in order to be successful. That’s another place where I spend some of my time.

Jeannette Guinn: Awesome. Then I guess I want to take it to… For all of be, what advice do you have for other women based on some of your experiences, your influences? I know that a couple people mentioned the importance, and Elena talked about it too, importance of having a mentor. I agree. Being a mentor and having one, the benefits of that just are endless. Dimitra, you talked about being influenced by Karna. I can say that the same has happened for me, so thank you, Karna, for everything that you’ve done for me. Just working on confidence, how to present in front of executives, how to become politically savvy, all of that is so important to growth. Dimitra, how would you like to expand on that?

Dimitra Papazoglou: Okay. I’ll share advice not really coming from my experience, but again, from women that talk about their stories, their experience through the women community. I’ll tell you three stories and what I have got from them. The first story was about the new role. There was a new role in her team. However, this role was in a different location, very far away from her location. Her manager never thought of her as a candidate because of the location, but then what she managed to do is to persuade that she’s the best for her role. No matter of the location, she actually managed to take the role. They found, together with her manager, a solution about the location issue and she actually got the role.

Dimitra Papazoglou: The advice that I got from that is that don’t wait to be given the opportunity, just believe in yourself and go and just take the opportunity. The second story is mostly advice. I’ll talk about my experience. I thought in the beginning that in order to go to the next level and get promoted, my manager actually will see that I’m doing awesome things and she or he will offer me the role, the promotion.

Dimitra Papazoglou: But then what I got through advice actually from another women was that when you want the role, just go to your manager, make it clear about what you want. Ask what you need to do in order to get the next role and just make sure that you take all the bullets and then just go to your manager and say, “I do all of this, so I can get the role.”

Dimitra Papazoglou: On top of that, she actually told me that even when you take the role, when you take the promotion, even then, go to next day and ask what you need to do for the next promotion. That’s also good advice. The third story that I want to share is about a pay rise. She wanted to get a pay rise in the beginning. She couldn’t really get it. She thought that she should give up, but then one thing that you said about mentorship, she had a great mentor.

Dimitra Papazoglou: In Cadence, we have great mentorship programs. The mentor was very, very supportive. Also through the community and, again, listening to other stories about similar topics and negotiations, she actually decided to keep trying. She got the confidence and then in the end, she got the pay rise. I will say just keep trying and never, never, never give up. That’s all.

Jeannette Guinn: Thank you.

Dimitra Papazoglou: I want to say that this advice… I’m sharing this advice because this advice has also influenced me and also has affected how I navigate my career.

Jeannette Guinn: Yeah. Yeah. Karna, what about you, influences, experiences?

Karna Nisewaner: I think one of the most important things is really just your own internal confidence and knowing that you are the best, knowing that you are capable of doing things and knowing that even if you don’t check all those boxes, you can check all those boxes if you’re just given an opportunity to try. I think back to several of the jobs that I got, where people were like, “Oh, you only got that job because you’re a woman.” I was like, “No, I got it because I’m better than you. I have more potential than you. I’m smarter than you.

Karna Nisewaner: I think feeling that and knowing that… Yeah, we’re all absolutely capable and you just need to internalize how capable and confident you should be because you can do it. You can absolutely do it. One of the pieces of advice I give to people is really just know your worth, know how valuable you are, know how much you can really do and do that.

Karna Nisewaner: I happen to have been raised in a family by a father that just made me feel super confident. I think that’s the best thing everybody can do is work on that however it makes sense to work on it. The other thing I to talk about is really work on building relationships with others. It doesn’t have to be anyone specific, but building the relationships across an organization will really help you grow your career because you’ll hear about things that are going on that you might not otherwise hear about. You’ll be able to make connections and help other people. Then in the future, they’ll know, “Oh hey, maybe I should help Karna.”

Karna Nisewaner: The other thing I would say is ask for things that you want. I wanted different experiences. I was focused in one area and I was like, “I want more. I want something else.” I said, “Hey… to my manager… “I want something more to do.” Then they gave me something more to do, and I did a good job with it, so then they gave me even more to do. I feel like you have to ask for those things because people don’t know what you want until you tell them. They can’t read your mind. They might say no, or it might not be the right time, but at least they’ll have that in their head and you’re no worse off by sharing what you want than you would be. You’re worse off not sharing really.

Karna Nisewaner:I just feel like raising your hand to say what you want, getting yourself out there… Being competent in your capability and ability to do any job that’s out there if just given the time and support to do it is really, to me, what I think is important that everybody kind of take away from this. Then as leaders and as members of the community, how can we help other people do that? How can we be the person that listens to what somebody’s saying in this, “Okay, this is what you can do. Let’s role play. Let’s make it happen.” I feel like that’s how we can really empower others is be that amplifier of other people’s voices. When somebody does something great, remind people, but then also shout out for yourself because you’re valuable.

Jeannette Guinn: I don’t know about the rest of you, but I’m pumped. I’m like, “I’m going to take over the world right now, Karna, that was awesome. Thank you so much and, Rishu, your thoughts.

Rishu Misri: I think pretty much whatever everybody else has already said, but my two cents will be just we need to make our tribe grow. For that, whatever it takes. Depending on where we are in our life and in our career path… If you’re in entry level, you will probably have to be focusing more on building your skills, trying to build the right networks. We’ve talked about mentorship and having that confidence. Like we say, that’s the most important thing, having the belief in yourself that you can do it, being resilient.

Rishu Misri: As you grow and are in a position to even be able to support others, then be compassionate towards the other women. Being a woman and being in a workforce, it’s not going to be easy. There are going to be times when it’s going to be tougher for you than it is going to be for your male counterparts. I mean, no offense there. I know everybody’s competent, but we’re going to be taking so many additional roles and nobody can take it apart from us.

Rishu Misri: I think it’s important that as a community, we stay more connected and we stay more compassionate towards each other and support each other in whatever positions we can and I think we also need to get more focused to bringing those women back who had to apply brakes to their careers. Be compassionate towards them. If there have been a lot of women who’ve applied brakes because they wanted to take care of children or they had had elder care to take care of or whatever other personal requirements…

Rishu Misri: If anybody had a career aspiration, a dream and we can help motivate those people back into the system, the workforce, I think that’s important. Just as everybody said, having belief in yourselves and just continuing to take the risks, I think that’s very important. Being able to try out new things and having the confidence that it’s… Tough times will be there, but I’m going to overcome them with my training, with my mentor support or whatever.

Jeannette Guinn: Yep, absolutely. Thank you Rishu, and as we wrap up this panel, last words of wisdom to women that are in the tech space that are working towards advancing their career… I’ll kick it off because it’s kind of wrapping up some of the things that you’ve all said. I say this to myself, to my team, to my family members. Don’t allow a struggle or a hardship to bring you down. It’s an opportunity or use it as an opportunity to grow stronger.

Jeannette Guinn: I could have a whole other session on my history, but I was financially on my own starting at the age of 17, and suffered years of abuse until I was about 23 years old. It sucked and you take each and every moment as learning opportunities and you make the best out of those crappy situations. Anything that I had to deal with in my 20s, as I was trying to advance my career, there were little nuggets of learning lessons.

Jeannette Guinn: If you want something, you go after it. Take that chance. There are going to be risks involved. There are going to be failures and that’s okay. You just don’t look back. You just keep looking forward. There’s a phrase that I use a lot. I say it a lot, but I was in a 12-month program with Women Unlimited, fabulous program. They taught me that you strive for excellence, not perfection because perfection’s just not possible. every day I just do my best and you strive for excellence. that’s my last words of wisdom. Rishu, any last words of wisdom from you.

Rishu Misri: I think I just continue build on what I said in my previous… I think it’s important that we continue to be resilient. That’s what is important. Just stay there, hang in, and if needed, seek support. There will be a lot of we people willing to help you. A lot of times, we may feel, “Am I doing the right thing being here? Is this where I should be? Maybe I should quit. Maybe this is not for me. Maybe… There’s so many questions that be come in to our mind. It’s not just for you. It’s for everybody.

Rishu Misri: Seek support. If you need to apply the brakes, do that. I’ve done that as well. When I had my daughter, I applied the brakes. Then when I had my son, I sought support. That’s ways I was able to continue doing what I wanted to do. I think that’s the other most important piece of advice that I have. That is whatever you choose to do in that moment. Do not be guilty about your choices.

Jeannette Guinn: Yes, yes, absolutely.

Rishu Misri: It was your decision. Don’t be guilty for whatever the choice you made. That’s important. Be resilient, seek support, don’t be guilty. That’s important. I think that’s all that I would say. Thank you.

Jeannette Guinn: Great. Thank you. Dimitra?

Dimitra Papazoglou: Yeah. For me, I’d like to actually say three things. For me, always have a career plan for the next two to five years and make it clear to your manager. Second thing, find the ways to strengthen your confidence. It can be this conference, it can be this panel. Find the Karna that will help you to have the confidence and say, “Okay, I’ll go for it. Karna said that. I’ll get all this confidence and I’ll go for it and I’ll take it.” The third is seek for opportunities. Don’t wait for them, okay? Don’t wait for others to give you the opportunities. You need to seek for them.

Jeannette Guinn: Thank you so much, Dimitra. And Karna?

Karna Nisewaner: I’ll build on what Dimitra said. It’s not just seeking opportunities. It’s being okay with change, being okay with saying, “This isn’t working out for me. I need to find a different environment, a different set of colleagues,” and having that community, having the people to support you.

Karna Nisewaner: I feel like you need to also be open to new things and maybe it’s a change in your role at a company. Maybe it’s a change of companies, but being flexible with yourself and not feeling like you’re stuck or stagnated into one thing, but that you can really do anything because I do believe that there are so many possible options for everyone. We just need to try and we just need to experience them. Sometimes things will be great. Sometimes they won’t be great. What can you change to make it better? Because you control your environment.

Karna Nisewaner: Yes, there are certain things we need. We need our paychecks, but you do control a lot of your environment and you need to create and find that environment that’s supportive, that’s there for you and that wants you to be successful. I feel like that’s what I found at Cadence is an environment where managers, colleagues, other people I worked with, they wanted me to be successful and they wanted to help me find that next thing.

Karna Nisewaner: You don’t find that in all jobs. If you’re not finding that, find people that will help you. Find a new role. Find others that will really amplify the value that you’re adding and really appreciate the way in which you add that value. I feel like we control our future, but we need to be out there saying what we want, sharing what we can do for others.

Karna Nisewaner: We can all have great careers. I just love how many more women are engaged and how many more of the underrepresented minorities are engaged in the community here at Cadence, are engaged in the Bay Area and are engaged worldwide. It’s great to see that growth. I just really hope it continues and that we continue to really show everyone that we are amazing. We are the best. We’ll rule the world, right?

Jeannette Guinn: Yeah, absolutely. Absolutely. I love it, Karna. Thank you so much, Karna, Dimitra, Rishu. It’s been a pleasure. On behalf of Cadence, thank you all. I hope this was helpful. Angie and Girl Geek, thank you for this opportunity. It was a wonderful experience. With that, go onto networking. Thank you so much.

Karna Nisewaner: Thank you.

Rishu Misri: Thank you.

Dimitra Papazoglou: Thank you.

Angie Chang: Thank you for being a part of that panel. I feel very empowered and ready to dig in. Now, I want to just really quickly plug that Cadence is hiring. They’re hiring for engineering jobs in cities like San Jose, California, Cary, North Carolina, and Austin, Texas. Now, we’re going to move onto our Girl Geek X networking hour. There’s a link that will be put into the chat. If you click on that, it’ll go to Zoom meeting, and we’ll see in a Zoom breakout room very soon.

cadence girl geek x speakers zooms
Cadence Girl Geek Dinner on March 16, 2022.

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Best of Elevate 2022 Sessions – From Decision-Making to Engineering Leadership, Mental Health to Career Growth

Our 5th annual Girl Geek X: ELEVATE all-day Conference on March 8, 2022 in celebration of International Women’s Day hosted over 3,600 around the world.

Here are the top 15 sessions from Elevate 2022, as voted on by attendees! You can watch (or re-watch) them at the links below, or watch the YouTube playlist:

  1. Decision-Making at Scale – Arquay Harris, VP of Engineering at Webflow
  2. Break the Bias: From Work to Mission – Leyla Seka, COO at Ironclad, and Jiahan Ericsson, Senior Director of Engineering at Ironclad
  3. Riding the Highs and Lows: Navigating Bad Mental Health Days in the Workspace – Ashu Ravichander, Principal Product Manager at Workday
  4. How to Get The Promotion You Deserve – Ali Littman, Director of Engineering at Modern Health
  5. Career Growth for Humans – Kristen Warms, Senior Manager, Learning Development at Atlassian
  6. Engineering Leadership – Jenn Clevenger, Senior Director of Engineering at Etsy, Kamilah Taylor, Head of Financial Products Engineering at Gusto, Willie Hooykaas-Baldwin, VP of Engineering at Salesforce, and Sukrutha Bhadouria, Director of Engineering at Salesforce
  7. Your Ableism is Showing: How You’re Missing the Mark By Not Including Accessible Practices Erin Perkins, Accessibility Educator and Founder at Mabely Q
  8. It’s A Hot Job Market. Do You Stay or Do You Leave? Aliza Carpio, Director, Technology Evangelist, Autodesk, Rocio Montes, Senior Engineering Manager at GitHub, and Sharon Hunt, Head of Product at Clovers
  9. Why Knowledge is the Future of DataMichelle Yi, Senior Director of Applied AI at RelationalAI
  10. You’re a Sales What? Life as a Sales EngineerMelissa Andrews, Sales Engineering Manager at Splunk
  11. How to #HumbleBrag EffectivelyShailvi Wakhlu, Senior Director of Data at Strava
  12. Tech is a Team Sport: When Women Lead, Everything is Possible Clare Martorana, Federal CIO at Executive Office of the President and Mina Hsiang, Administrator at United States Digital Service
  13. Launching and Leading Cross-Functional Initiatives as an Engineer – Izzy Clemenson, Senior Staff Engineer and Tracy Stampfli, Principal Engineer at Slack
  14. Become the Role Model You Wish You Had – Reeny Sondhi, Chief Security Officer at Autodesk, and Susanna Holt, VP of Strategic Technologies at Autodesk
  15. Economic Justice and Cryptocurrency / Web3 – Jen-Mei Wu, Community Organizer at PaRTEE4Justice

Special Thank You To Elevate 2022 Sponsors and Government Participants!

Thanks to the great folks at Atlassian, Slack, StravaAutodesk, Front, Intel, IroncladMosaicML, Opendoor, RelationalAI, SplunkUnited States Digital Service, Fisher Investments, Meta for supporting the 5th annual Elevate virtual conference for International Women’s Day!

Don’t forget to check out their jobs—they are actively hiring!

Top 10 Tech Talks from ELEVATE 2022 Conference

Our 5th annual ELEVATE Conference sessions are online! Watch the conference talks on our YouTube playlist. Scroll through the speaker highlights below as we’ve re-watched, conducted attendee surveys, and found you the BEST sessions to watch from Elevate 2022!

We’ve compiled the best of 2022 Tech Talks, from NEW TECHNOLOGY (and the latest startups), to tactical advice for ENGINEERING LEADERS – both individual contributors and managers!

#1 – Michelle Yi, Senior Director of Applied AI at RelationalAI, talks about why knowledge is the future of data. Harnessing knowledge and data together leads teams to faster modeling and insights. In this session, she demos relational knowledge graphs.

#2 – Julie Choi, Chief Growth Officer at MosaicML, in conversation with Laura Florescu, AI Researcher at MosaicML, about their unique career paths to machine learning. Laura is working on accelerating neural network training with research in natural language processing and computer vision, combining multiple algorithms to train models faster. Check out the Composer open source ML library on GitHub.

#3 – Izzy Clemenson, Senior Staff Engineer at Slack, talks about leading and launching a product as a IC, along with Tracy Stampfli, Principal Engineer at Slack. The engineers talk about large projects they led at Slack, tips for getting stake-holder buy-in, and metrics.

#4 – Melissa Andrews, Sales Engineering Manager at Splunk, talks about Sales Engineering (SE) as a career for mid-career women with a curiosity for tech. She talks about the confusing set of job titles SEs can have, the skills and activities for the role, and how to get started!

#5 – Maria Lucena and Divya Mahajan, Directors of Architecture at Fidelity Investments, discuss AWS, GraphQL, with Apollo: Vue.JS delivering enterprise grade-applications. They review the architecture for a project, explaining the decision-making process behind the tech stack.

#6 – Ashu Ravichander, Principal Product Manager at Workday, builds resiliency into her professional life to successfully navigate the ups and downs of a mood disorder. She lays out tried-and-true toolkits, playbooks, and best practices for setting herself for success on bad mental health days to bring her best self to work, every day she needs to.

#7 – Ali Littman, Director of Engineering at Modern Health, shares scaffolding for getting promoted. She lays out alignment with manager, understanding the career path at your company, having a growth plan, asking for regular feedback, and effectively sharing your achievements across all levels (manager, department, and company).

#8 – Jen-Mei Wu, Community Organizer and Founder, balances healthy skepticism with her excitement for the web3 opportunity to address financial inequity. She reveals different ways to make a difference with a small and mighty entrepreneurial team (e.g. decentralized finance helping fund non-profits, dealing with carbon).

#9 – Arquay Harris, VP of Engineering at Webflow, asks how do you know what is the “right” decision? Which actions are easily reversible and which cause irreparable harm? Perfect is not the goal.

#10 – Mina Hsiang, Administrator at the United States Digital Service, and Clare Martorana, Federal CIO, discuss the challenges of government tech, and how they are building a team transforming government services for millions by putting people at the center of everything they do.



Check out the top 10 ways our speakers spoke about interrupting bias (this year’s #IWD2022 theme) and blazing a new path forward. 🏆

“Economic Justice and Cryptocurrencies / Web3”: Jen-Mei Wu, Community Organizer at PaRTEE4Justice (Video + Transcript)

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

Angie Chang: So our final session is Jen-Mei Wu, who will be talking about cryptocurrency and how it is a lever for inclusion and economic justice. Welcome, Jen-Mei.

Jen-Mei Wu: Hi everyone. Welcome to the talk about economic justice and cryptocurrency. My name is Jen-Mei, and I will tell you a little bit about myself. I am based in Huchiun, an unceded Lisjan territory, aka Oakland, California.

Jen-Mei Wu: I’m an activist, an engineer, an artist, and one of the founders of LOL Maker Space in Oakland. Also, Power and Resilience Through Experiential Education, our PaRTEE4Justice, and I have a bunch of experience working both in non-profit tech and also in for-profit techs as well. And I have a particular interest in digital privacy and security. And part of that is what brought me into this space. So this is just an overview of what I’ll be over today.

Jen-Mei Wu: We’ll be talking about an opportunity to address inequity in financial systems and going over some examples of projects that are making a difference in this space, and how small teams can have really big impacts and that there’s a role for everyone. So including you, if you want to come and join this stuff.

Jen-Mei Wu: I’m going to talk about the importance of self-determination and also some collaborations that I’m doing with Black Voices and Gaming and Sistah Scifi, as well as the future, which is the decentralized web or Web3, as you may have heard. And also about how to get started in this space.

Jen-Mei Wu: So this slide here is showing the market cap of cryptocurrencies altogether. As you can see, just in the last two years, we’ve had more than a trillion dollars flood into the space. A trillion. That’s a trillion with a T. That’s a lot of money. And actually, at some point last year actually, it was two trillion dollars, and I’m guessing it will get there again in the future. And so the question is, so all this wealth is being traded, but who’s benefiting? Well unfortunately, right now, cryptocurrency isn’t the most diverse space. It’s not the most inclusive space, and this talk is about how that can change.

Jen-Mei Wu: So moving on, before I get into the opportunity though, I just want to share a quick note doubts. So when I talk to people about cryptocurrencies some of them are really excited. But others are like, “well, but what about the dark web?” Or isn’t it bad for the environments? Or are there like a lot of tech bros running around doing tech bro things? And most of all, “isn’t it all made up? Is just fictional? Is there any value there?” So I’ve done my own research, encourage you to do your own research. I’m not particularly concerned about these. And in some cases I see some of these challenges, which are real and are legit, as being opportunities for us to make some really big and important changes.

Jen-Mei Wu: I just want to share what I found to be an inspiring experience that had to do with another disruptive technology, and that would be 3D printing. Some friends a few years ago, I think it was like eight, nine years ago, were really concerned that 3D printing is a technology that could take jobs away from workers. It’d be an opportunity for large corporations to create 3D printers and the materials that go into those 3D printers, and have people make stuff at home. Therefore, centralizing control and hanging out to the profits, which is ironic because 3D printing was meant to be decentralized and meant to be hyper local. And that’s kind of attention that you see and cryptocurrencies.

Jen-Mei Wu: But the interesting thing about this story is that a few days later, I went to a talk by Grace Lee Boggs, who’s very amazing. I encourage you to look her up if you don’t know who she is. And it wasn’t a talk about 3D printing, but she mentioned 3D printing. And what she mentioned was that she, and she’s 99 years old at this time, this is about a year before she passed away, and she said that she saw 3D printing as being really exciting because it could be a way to end capitalism. So she saw how 3D printers could be used to create other 3D printers and how a lot of initiatives are helping people create materials to feed into their 3D printers locally without having to rely on filament companies. And so, she was able to think big, and that’s what I would like us all to do is to think big. Think about how challenges can be opportunities. And in that spirit, I’m going to talk about what some of those opportunities are.

Jen-Mei Wu: We talk about some projects making a difference, just to give you a sense of the space. This is a screenshot from the Movement for Black Lives webpage. They take cryptocurrency donations, and they do that through this project here called The Giving Block. The Giving Block allows organizations to accept crypto and individuals to donate crypto. And that’s great. Endaoment is another website that’s similar. Except with Endaoment, they don’t already have to have signed up to accept crypto. You can give to Endaoment, and it’ll create a donor-advised fund for you. I’ll let you do some research on your own about what a donor-advised fund is. But the idea is that you can donate to Endaoment, a nonprofit organization, and then later have those funds go over as regular cash to the organizations that you want to support. For example, the Sogorea Te’ Land Trust. Sorry, I have to adjust my image. That’s for straightforward, right? That’s like, “Hey, you can give cryptocurrencies away.”

Jen-Mei Wu: Where it really gets interesting is when you can start doing some things that are pretty unique to decentralized finance, which is built on top of the blockchain. So this is an example of Angel Protocol. And their idea is that instead of donating directly to a nonprofit. You donate to a nonprofit’s endowment and Angel Protocol will reinvest those funds, generate a yield, and that yield can go to fund the nonprofit’s operations, potentially funding that nonprofit for forever. And the way that they’re able to do that is Angel Protocol is on the Terra Blockchain, which is a low energy blockchain FYI, and another protocol on that blockchain is Anchor protocol.

Jen-Mei Wu: And Anchor protocol is a savings account where people can take their U.S. dollar equivalents in UST. It doesn’t go up or down like some volatile cryptocurrencies does, it’s always equal to a dollar, and you can get 20% interest on them. Which is pretty amazing when you compare that to the savings accounts that banks typically offer. And that’s just an example of how one protocol, Angel, is able to leverage another protocol, Anchor, in this case.

Jen-Mei Wu: And another example of that is… I’m going to have to do this again. Is carbon credits. So Moss and Toucan, are two projects that have put carbon credits on the blockchain. Carbon credits are created by things like forestry initiatives, things that are good for the earth, and are purchased by polluters like airlines or manufacturing companies to offset the emissions that they do so that they can claim to be carbon neutral. And that’s interesting, just being able to buy your own carbon credit. So you want to be carbon neutral yourself, just like the airlines, so you can buy your carbon credits on the blockchain real easily. That’s great.

Jen-Mei Wu: But it also created at a building block for KlimaDAO to create a black hole for carbon. Using game theory, KlimaDAO has encouraged people to invest in locking up carbon so it is off the market. And the idea that if it’s off the market, then it becomes harder for companies to get carbon credits. It’s more expensive, which will either generate more money for the forestry initiatives and others, or maybe it’ll encourage companies to think about other ways to reduce emissions, such as by reducing emissions.

Jen-Mei Wu: And I also just want to mention that some of these projects that I mentioned are maintained by small teams. There’s many projects, in fact, that are maintained by small teams. So I’m used to, as many of you might also be used to, being in the tech space… Especially here in the Bay Area, there’s a lot of venture funding and a lot of startups are actually pretty big and require a lot of funding in order to make their work possible. But because projects can be small teams, that means that they have smaller budgets. A smaller number of people can do really interesting things. And protocols can build on top of each other so they can have a very narrow focus. And that also makes things a little easier and helps facilitate the small teams.

Jen-Mei Wu: And there’s a lot of different project types. Sure, there are big projects that have hundreds and hundreds of numbers, and thousands of numbers, maybe even. But there’s many project types that have maybe even one member. There are some artists who have created their own NFT projects. NFTs are non-fungible tokens. They’re basically a way to track art on the blockchain. So you always know the provenance of your art, or whatever, and where everyone’s unique and cannot be duplicated. But there’s individual artists who started these projects and don’t even have other people helping them out. That’s not everybody, but it has happened.

Jen-Mei Wu: And that brings me to my next point, which is just there’s a role for everyone. There are engineers and designers, of course. There’s some very lovely web apps that I showed you that engineers and designers did a big part in, but also game theory, as with KlimaDAO. There is a role for artists in NFT projects and other things as well. But also for community organizers, community is the lifeblood of many of the projects that exist. And people who facilitate community connections, who run events, who help connect other people together. They’re very important, and they come from a variety of backgrounds. Likewise, the space really needs a lot of educators because there’s a lot of unknowns in this space. And it’s important for people to have easy on-ramps. To be able to learn how to participate and about the different projects that are always coming out. There’s always something new as well as analysts and more people. So these are just some examples.

Jen-Mei Wu: And one of the things that I’m really excited about is trying to get away from this slide, which is funding with compromises. Which I feel is what a lot of us are used to. If you work for a VC funded tech startup, you’ll probably have noticed that VCs have the big influence on how tech companies survive. VCs are not after making companies successful. They may disagree with me, but I would say that they’re into helping companies gamble so that their successes are ginormous, but some of them are just going to explode. And in so doing, they pump funds into companies that allow companies to hire above market and at a very high pace. Bringing people from, for example, out of the area, into the area. This has fueled displacement and gentrification that we have all noticed, at least those of us in the Bay Area and other places where this phenomenon has happened. Foundations, similarly, have an influence on nonprofits. Many nonprofits get a significant amount of their funding from one or two or three foundations. And sometimes find that their priorities must be affected by what the foundations want.

Jen-Mei Wu: I see crypto as a way of moving towards funding without compromises, or at least with fewer compromises. Of course, you can do grassroots fund fundraising. You could have people contribute, for example, to an Angel protocol endowment and operate off of that. Or you could create an income generating project, some of which do not require years of development or even months of development, or even weeks of development. You can build projects, but there’s many other opportunities as well, like validation, NFT projects, content creation.

Jen-Mei Wu: And if we can fund our projects without compromises, then we can work towards self-determination. And just another example, like DEI efforts. I have been involved in DEI efforts at tech companies, many of you may have as well, and they’re great. They get people a seat at the table. And they get people in income and that cannot be understated. That’s really important. But having a seat at the table isn’t as good as owning the table, and it’s not as good as being able to design or build something, which might or might not be a table. Right now, if you feed into the existing infrastructure, then you’re playing by their rules, but self-determination means making our own rules.

Jen-Mei Wu: And with that, I’m going to mention a couple of collaborations that I am excited about. Alfonso is one of the folks behind Black Voices in Gaming. He’s also one of the other founders of Liberating Ourselves Locally, the maker space I mentioned earlier. He has this program where he teaches black and brown youth in Oakland how to do DevOps, which is pretty amazing, and we are going to be looking at building on that curriculum, and combining some crypto activities with that as well, to create a program that generates income, for example, by validating blocks on the blockchain.

Jen-Mei Wu: And the idea is that when students come in, and we’ll obviously have to do some investment upfront, and create some of that infrastructure. But when they come in, they can learn while also potentially getting paid to learn. The idea is that… Coding schools, great, but also not everyone can afford $20,000 or whatever in tuition and to take months off work. But if you can get paid to learn, that really changes the game. Now the work that they would be doing would be something that they can do on their own as well. And so they don’t have to join a VC funded startup in order to succeed. They could just keep doing this stuff, self-determination.

Jen-Mei Wu: So the second collaboration is with Isis, Sistah Scifi. She sent me a little video that I’m going to play for you so you can learn about her.

Isis Asare: Hi, I’m Isis Asare. And I just wanted to take a second to introduce you to Sistah Scifi, your new favorite book boutique. We focused on science fiction written by black women. In addition to selling books, we also host a lot of online and in-person events, book clubs, watch parties, you name it. It’s a black, queer own company. It’s owned by myself.

Jen-Mei Wu: What we’re doing is we’re starting an NFT project, which is an opportunity for us to diversify the NFT space. To tell our stories, to fund our initiatives, and increase representation and create opportunities for artists who are not currently in this space. NFTs are like other parts of crypto, not extremely diverse.

Jen-Mei Wu: That brings me to the future. So right now there’s a lot of stuff. A lot of promises in crypto that are not quite realized yet. Like the decentralized web or web3, it’s definitely not here yet. A lot of the websites I showed you are traditional websites. They run on the same hosting that you’re probably used to. If that hosting goes down, the website goes down, and unless people know how to interact with smart contracts on the blockchain directly, it’s definitely not decentralized.

Jen-Mei Wu: But in the future, hopefully there will be a future where corporations don’t control the infrastructure. Where the infrastructure will be distributed and decentralized across the world, and it won’t go down when a single computer goes down. It’s still being defined, and now is a good time to be part of that definition.

Jen-Mei Wu: So here’s some information about how to get started. Social media is a great place to learn about this stuff, but I would consider being anonymous because there are scammers out there. In any extremely non-diverse space, there’s some potentially unpleasant interactions that can happen. I, myself, interact with crypto communities using anonymous accounts, not the ones I’m about to show you. And also suggest some healthy skepticism. I think there’s a lot of opportunity here, but I think it should be balanced with some skepticism as well. And don’t be too quick to trust folks because people will offer to do tech support for you or show you all sorts of things, but they do not have your best interest in mind.

Jen-Mei Wu: I would suggest joining communities that are supportive. And there’s new communities starting all the time. Some of these are communities for women, non-binary folks, LGBTQ+, people of color. And also, we will be having a future workshop at PaRTEE4Justice.org, and you can check out our website to learn more about that.

Jen-Mei Wu: That brings me to the end of the presentation. I hope you go and build stuff. I hope you keep in touch. These are my socials. I’m not really the best at them. Feel free to message me more than once if I happen to miss your message. And a link to the website PaRTEE4Justice.org.

Jen-Mei Wu: And that brings me to the end of my presentation. I do see there’s a question, some questions, but I don’t think I have time to answer them. So I’m going to suggest that you follow up with me, and I can answer those questions, and I’ll hand it back to Angie.

Angie Chang: Thank you, Jen-Mei, for that talk on crypto and web3, and definitely see some possibilities. I like your argument, and I am going to look at all those projects that you’ve mentioned later and see how I can get involved. Thank you for sharing.

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“Why Knowledge is the Future of Data”: Michelle Yi, Senior Director of Applied Artificial Intelligence at RelationalAI (Video + Transcript)

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Angie Chang: So our next session is Michelle Yi from RelationalAI. She is a Senior Director of Applied AI, and she’ll be speaking about harnessing knowledge and data and show us relational knowledge graphs in action.

Michelle Yi: Great. Thank you so much, Angie for the introduction. I’ll just go ahead and screen share, make sure everything is going smoothly. And let me make this big. Here we go. Okay. I think we are good to go. Okay. Yes. So happy International Women’s Day, everyone. And I, plus one, I saw in the chat, a comment about Reggie for president. So plus one to that.

Michelle Yi: So my name is Michelle Yi as Angie said, I’m super excited to share a little bit of perspective on why I believe knowledge is the future of data and how my personal experiences in the data space also align to this common vision that brought me to RelationalAI.

Michelle Yi: And so I thought I could start with sharing a little bit of context and background on myself and the journey that has brought me to RelationalAI. Our vision for what we’re doing really spoke to a lot of the challenges and problems that I saw in the machine learning and data space.

Michelle Yi: And actually to make this a little more fun and interactive, if you guys want to share a little bit about your own journey technology. I’d be curious to see what did you all study, whether it’s undergrad, PhD, masters.

Michelle Yi: What was your last educational focus? Something Heather said in the last talk actually really ties to the demo I have at the end of my talk, which is going to show a little bit of the backgrounds of the women that signed up for this conference.

Michelle Yi: So for me personally, I spent the last 16 years or so in the AI/ML space working with data from both our products R and D side, as well as a consulting perspective. So specifically, I don’t know if anyone will remember this, but in 2012, one of my first projects that I worked on actually aired as IBM Watson and this whole thing with Jeopardy where the computer was playing against the humans.

Michelle Yi: So that’s my one claim to fame. And then after that, I moved more into management consulting because I really wanted to understand the data and data science problems that many customers across many verticals were facing. And so through these experiences over the last couple of decades, I really got a lot of exposure to the impacts of the constantly shifting technical paradigms and how that impacted business.

Michelle Yi: So to give you an example, when I started at IBM ML 16 years ago, was on mainframe. This was before the Cloud. If you can even imagine an era before the Cloud. And then we were after we started getting migrated and pushed to go more Cloud oriented, moving away from on-prem, there was a big, no pun intended, big data movement.

Michelle Yi: Essentially saying, like, “Go collect all the things.” And we didn’t… We collected a lot of data without really always thinking about why did we need that data? And then we were sort of pushed to like, “Okay, well, if you want to use this big data thing and you want to make all those things that you collected useful, you need to go to MapR and Hadoop.”

Michelle Yi: And then what ultimately resulted was this data swamp architecture where we had data everywhere in different silos of many different types. And then that shifted into what’s now more of the modern Cloud data warehousing. So think about BigQuery, Snowflake, Redshift et cetera. And then after we consolidated all of these things, we’re like, “Oh, okay. We finally got it figured out.”

Michelle Yi: But then you kind of see another kind of paradigm around machine learning and for people to take advantage of that you need yet another patchwork tool chain. And we’re going to dig into this a bit more, but the question is why is it that every time we see a paradigm shift, or a new technology, or a new data structure that we kind of go through the same motions over and over again.

Michelle Yi: And so just to speak to a little bit of those problems, I don’t think this is going to be new to anyone in the data space, but basically with each iteration that we’ve gone through, we still see the same needs from the business and the technology side.

Michelle Yi: There’s this desire for kind of more data driven decision making across the board from your executive teams all the way down to the engineering teams. And then there’s this other problem of like, “All right, we went through big data and we collected all the things, but now we don’t really understand everything that we’ve collected.”

Michelle Yi: So we even today, I think many of us would agree that there’s really kind of a lack of understanding of the full extent of the data assets that an enterprise or even a startup has.

Michelle Yi: And then as a result of that, there’s this third bucket of problems where we’ve really seen a rise of just too many point solutions or too many point data applications that sometimes can be repetitive of each other.

Michelle Yi: I don’t know how many times I’ve [inaudible] this and seen to a customer and we’re like, “Hey, you’re interested in a fraud detection, no problem. Oh, by the way, they also built their own fraud detection solution over there in teams D or E.” And so we’re kind of seeing like this common theme across companies and across a long period of time. And again, we need to ask ourselves what’s the root cause of this.

Michelle Yi: And ultimately I think what I saw over and over again is that there’s really something missing from this modern data stack. If we’re really evolving the way that we think about data, why are we seeing the same problems manifest over and over again? And so this is the question I really want us to kind of hone in on and specifically around this concept of knowledge and I’m going to share because you’re like, “All right, knowledge.” That can mean so many different things to basically everybody on this call.

Michelle Yi: And I’d be curious how many data scientists, more on the ML side we have in the room today versus more of the software engineering data app side, I’ve lived in both sides of those worlds. And they’re converging in many ways, right?

Michelle Yi: Because a lot of intelligent data applications today at the core of them, they really are having embedded machine learning whether that’s a machine learning model that you and your teams build or managed service that you receive from a vendor that you buy.

Michelle Yi: And so from my personal experience, I wanted to share an example of a day in the life of a data scientist or a software engineer working on an intelligent application and really hone in on this question using a workflow example of like what happens to the knowledge. And tell me in the chat, let me see.

Michelle Yi: I want to make sure I have it topped up in the screen, but please tell me in the chat if you resonate with this, but one common thing that I think people really have experienced is that we tend to spend like 80% as a data scientist or someone building an intelligent app.

Michelle Yi: We spend like 80% of our time productionalizing things and maybe 20% of our time really modeling, collecting the requirements and the data, et cetera.

Michelle Yi: And if I go into this just like one more level deep and not to get too trapped in the weeds, but just to really hone in on the pain point and why knowledge and embedding knowledge in a workflow is so important is let’s say like all of us are on the same team together.

Michelle Yi: And we want to build this fraud detection application. And at the heart of this application is a machine learning model that gives some predictive score of like, “Yes, that transaction is 50%, 60% likely to be fraudulent.”

Michelle Yi: Well, let’s think about this. So step one, what do we really go do? We let’s say one, we get a sense of our own intuition of what kind of data we need. We probably need something about transactions.

Michelle Yi: And we probably need something about accounts and people related to these transactions and maybe that lives in, I don’t know, BigQuery, let’s say it lives in Teradata, and then it lives in Excel because how many of us store data… Plenty of us store data at Excel. And then let’s also say that we probably need some information from the public web because when people steal things, they need to go sell them and make money.

Michelle Yi: So we get this intuition, we make a list. And then we ultimately, what we end up doing is we go to the business owners or the business experts and saying, “Okay, does it make sense to have this kind of data? What are we missing? Oh, I see, this data has this flag that has a transaction type one. What does that actually mean?”

Michelle Yi: And so we spend a lot of time upfront collecting and gathering data. We work on a subset and that in this 20% bucket of data science work, in that 20% of time, we get a model working that we’re pretty happy with.

Michelle Yi: Let’s say we use Python and a Jupyter Notebook. steps one and two are done. We’re happy. And then we need to scale this up to production. And then what we end up spending 80% of our time on is rewriting everything that we learned in terms of collecting the knowledge from different business stakeholders and our own data science knowledge.

Michelle Yi: And we rewrite that in like Java, Spark and much more heavier imperative programming languages, just so we can productionalize what we already did in steps one and two.

Michelle Yi: So the question is why can we not preserve knowledge across the data, across this entire workflow end to end. And that’s where I really kind of started to think more about this problem, because imagine how many like teams, how much time it would save if I could just preserve all of my learnings that I collected up front from the business about the relationships between transactions and customers, and accounts, and then also like the different constraints.

Michelle Yi: So for example, if I am looking for pictures of cats, I know that cats have two ears. I shouldn’t even think or waste any time processing things with four ears or five ears. I mean, this is a toy example, but I think you get the idea. And then 0.3 is really like, “Okay. If I on team A, I’m building this fraud detection app, why can’t I just easily share this knowledge with somebody in team D so that they don’t have to go do the same requirements gathering?” Because you know, that happens in any organization. And so when we talk about knowledge, it’s how do we preserve these relationships and really save ourselves time and.

Michelle Yi: We preserve these relationships and really save ourselves time and make that accessible to more than just one team. So, there is this concept of a knowledge graph and so you’re like, “Okay, well, yeah. I’ve heard about knowledge graphs.”

Michelle Yi: And there’s sort of like this way of structuring and thinking about data that can somewhat solve this issue, but not exactly and let’s… I want to get into that a little bit really quickly.

Michelle Yi: And so, one of the things is that, here is just an example of a knowledge graph concept, right? And the thing about this picture is even if I don’t give you all the details of like, oh, this lives in inquiry [inaudible], this one lives in another database.

Michelle Yi: Conceptually, you can kind of get that a product has a brand and a product has a category where shoes is an example of a category and a company sells products. It doesn’t matter if you’re an engineer or a business person, you can pretty quickly see what this is.

Michelle Yi: And now imagine if you could actually just query your data as easily as you can read this picture. The thing with knowledge graphs though is that they’re actually not necessarily a new concept.

Michelle Yi: So, it was coined by Google when they created the Google knowledge graph. They wrote this paper that came out in 2012, over 10 years ago now, and it’s been a core competitive advantage to them.

Michelle Yi: So if you ever wonder why search is so powerful at Google, this is one of the secret sauces to that. And when you’re shopping on Amazon, if you’re like, “Wow, my recommendations are amazing.”

Michelle Yi: That’s also another reason why they’re so powerful, is that they’re using this thing called knowledge graphs. And so a lot of other companies have really adopted this thing called the knowledge graph. And you’re like, okay, you can do all these cool things. You can express your business knowledge in the same place as you would do your programming or your data querying, why isn’t everyone else adopting this?

Michelle Yi: Well, the problem is that, and there’s many, many problems, but there’s kind of like three that all high level boil it down to. But one of them is that yes, knowledge graph expertise is kind of rare and not everyone is Google or Facebook or LinkedIn, and they can’t hire hundreds of engineers to go build these things for them, right? There’s not enough people out there to do this.

Michelle Yi: And the second thing is that building and scaling knowledge graphs is really difficult because a lot of the existing solutions are built on really old paradigms. So like the Google knowledge graph paper came out 10 years ago, a lot of the commercially available systems today make it hard to use.

Michelle Yi: Some of these systems are based on theories that came out in the seventies in terms of navigational systems, right? And so it’s really, really hard to use any existing thing to build your own knowledge graph if that’s really what you want to do. And so similarly, operating and maintaining them is really challenging as well.

Michelle Yi: So it’s an amazing concept that just really hasn’t been more commercially viable and accessible to a broader audience. And so, there’s one thing that I want to quickly over is we’re kind of taking a slightly different take and then I’ll show a really fun example to make this more real and in honor of international women’s day here.

Michelle Yi: But one of the things that we’re trying to do is say, let’s build that next generation thing. What does that really look like if we were to take a knowledge graph and make that supercharged and really available to a broader audience.

Michelle Yi: And one of the things that’s key is you see the word knowledge graphs, and then you see this thing called relational and RelationalAI. So I’ll share a bit more before jumping into the demo quickly and then wrapping up.

Michelle Yi: But essentially when it comes down to what we’re trying to do is build this next generation database platform that really gives you that infrastructure layer that’s going to help you consolidate and keep knowledge in the end to end workflow based on a solid shared foundation of a relational knowledge graph.

Michelle Yi: So one of the things that being a relational knowledge graph does is, and this is a bit of an eye chart, but I’ll summarize it in one point, which is that the relational paradigm, when you think about why SQL databases, for example, or you think about why snowflake or BigQuery or Redshift are so popular today is because it separates a lot of the what from the how. So you don’t worry about this huge list of super technical things in the middle, right?

Michelle Yi: A lot of that is actually handled for you. And so that’s something that’s really, really cool about a relational knowledge graph versus other systems. Because again, we share those same technical foundations of what you really expect from that modern data stack and including things like warehousing, et cetera. And so when you think about your favorite SQL system or your favorite database system, I guarantee a large part of that adoption is because your business users, not just your engineering teams, can use it.

Michelle Yi: And so in the future what we’d love to see is like, because we share these same fundamental architectural paradigms, we’d love to see that layer of knowledge that sits across and really pairs with and augments the work that many organizations have already done to consolidate and clean up their warehouse. Basically all the work that everyone’s done going from Hudu to cloud data warehousing, et cetera. This is the thing that we want to say is missing from that modern data stack and that we want to augment and really bring out the power of these things across your organization.

Michelle Yi: All right. So with that said, I’m going to take a look at the chat here and just see at some of the backgrounds. Okay. I love it. Business management, psych. All right. So, in the last three minutes or so I want to wrap up again with just like a simple example where we took some data, thank you to Girl Geek X for providing some of this as well.

Michelle Yi: But basically we took some data on the types of folks we knew would be presenting and attending the conference today. And then we also took some information that’s already… So, for those of you that don’t know about DIFA, we took some information from them. They actually structure all of the information on the public internet in a knowledge graph. And so it’s super easy for us to be able to leverage that in our system. And we took a high level view of kind of the women participating.

Michelle Yi: And basically what you’re seeing here is we put a visualization of what’s called the weakly-connected components graph, right? And so it’s a type of graph algorithm where what you can see quickly is like there’s certain densities and there’s certain areas that are less connected on the edge here.

Michelle Yi: And so we took a survey of sort of what did people study, right? And for women that are in engineering or technology, what did they study as the most recent education? And so what I thought was really fun about this is that when you zoom in, you can kind of see the clusters you might expect.

Michelle Yi: This is a New York if I remember right. And then in New York, there’s lots of people with computer science degrees, et cetera, et cetera. But when you get to the edges a little bit further out, you see a lot of really, really cool majors and folks of women that are in our fields and that have really, really diverse backgrounds.

Michelle Yi: And I love seeing this. So you see like economics, I saw English, English literature. I saw health informatics right here, design and art direction. And so I thought this was like a really fun way using knowledge graphs to quickly show that it doesn’t matter what background you have, but there is a place for you in tech.

Michelle Yi: And the thing is that when you are kind of one of these weakly-connected components, you might sometimes feel like you’re the only one. Right? But actually it’s not true. There’s so many of us that are out here.

Michelle Yi: And so I thought this was a fun way to show that using some real data. So yeah, I thank you so much for all of your time. I think we’re right at the 45 minute mark. And so, really appreciate it. And if you have any questions or you’re interested in graphs or the tech, please don’t hesitate to reach out. Thanks so much.

Angie Chang: Thank you, Michelle. That was very informative. I love the chart and the graph and for explaining everything so clearly. 

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