SPEAKER

Ha-Kyung Kwon

Ha-Kyung Kwon is a Research Scientist at Toyota Research Institute (“TRI”). As part of TRI’s Accelerated Materials Design and Discovery team, Ha-Kyung Kwon is building machine learning tools capable of rapidly advancing the discovery of new materials for batteries, fuel cells and other critical components for use in future zero-emissions vehicles. Ha-Kyung earned Bachelor of Science and Engineering in Chemical and Biological Engineering from Princeton, where she graduated Magna Cum Laude. She earned her Doctor of Philosophy (Ph.D) from Northwestern University in Materials Science and Engineering, where she wrote a thesis on equilibrium phase behavior of ionomers. While pursuing her doctorate, Ha-Kyung was recognized as the Internal Institute of Nanotechnology (IIIN) Outstanding Researcher (2016), a Center for Computation and Theory of Soft Materials (CCTSM) Fellow (2016) and a Weertman Fellow (2017). Throughout her research career, Ha-Kyung has specialized in polymer research, including theoretical and experimental investigation of polymer phase behavior. Her work at TRI enables her to stay closely aligned with deep foundational research while working in a fast-paced development cycle to accelerate the discovery of new materials.

VIDEOS

VIDEO

Girl Geek X Toyota Research Institute

Learn how TRI is working to build an uncrashable car, use robotics to amplify people’s capabilities as they age and leverage artificial intelligence to enable discovery of new materials for batteries and fuel cells.
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VIDEO

Accelerating Materials Discovery by Helping You Fail Faster - Ha-Kyung Kwon, Research Scientist, TRI

As part of TRI’s Accelerated Materials Design and Discovery team, Ha-Kyung is building machine learning tools capable of rapidly advancing the discovery of new materials for batteries, fuel cells and other critical components for use in future zero-emissions vehicles. She’ll share her insights about what makes TRI a unique place to work and her rare opportunity to couple disciplined and intensive research with cutting edge machine learning techniques which will enable humans to fail faster and, thereby, accelerate discovery.
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