Stanford University
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Jessica Lee Mega
Affiliate, Medicine - Med/Cardiovascular Medicine
BioJessica L. Mega, MD, MPH is a leader at the intersection of technology, life science, and health. She is a Cardiologist at Stanford and serves on the Advisory Board for Stanford's Center for Digital Health. She is a Co-Founder of Alphabet's Verily and former Chief Medical Officer of Google Life Sciences. She is on the Board of Directors at Boston Scientific, as well as the Board of Advisors for Research!America and the Duke-Margolis Center for Health Policy. She is a Senior Advisor at SandboxAQ and the Chair of the Investment Committee of the American Heart Association’s GRFW Venture Fund.
As a faculty member at Harvard Medical School, a Senior Investigator with the TIMI Study Group, and a Cardiologist at Brigham and Women’s Hospital, Dr. Mega led large, international, randomized trials evaluating novel therapies and new medical technologies. She directed the TIMI Study Group’s Genomics Program, demonstrating and testing the role of CYP2C19 genetic variants on antiplatelet medications, a key pharmacogenetic finding. She has published manuscripts in the New England Journal of Medicine, Lancet, and JAMA. She served as an Advisor for the California Governor’s Precision Medicine Initiative and the Board of Directors at Danaher Corporation.
Dr. Mega is a graduate of Stanford University, Yale University School of Medicine, and Harvard School of Public Health. She completed Internal Medicine Residency at Brigham and Women’s Hospital and Cardiovascular Fellowship at Massachusetts General Hospital. She is board certified in Internal Medicine and Cardiology. She has won the Laennec Society, Samuel A. Levine, and Douglas P. Zipes Awards, and she is a Fellow of the American Heart Association and the American College of Cardiology. -
Apurva Mehta
Senior Scientist, SLAC National Accelerator Laboratory
BioI am a materials scientist with three decades of experience unraveling the molecular-scale processes that govern the functionality, aging, and failure of complex materials and devices. Over this time, advanced characterization methods have undergone a revolutionary transformation, driven by the emergence of brighter sources—from synchrotrons and X-ray free-electron lasers to MeV accelerator-based electron sources—paired with faster and larger-area detectors. While the depth and precision of measurements have vastly improved, the explosion of raw data now poses a significant challenge, making it increasingly difficult to extract meaningful insights them.
Recognizing this growing challenge, I have devoted the last decade to harnessing the power of emerging machine learning and artificial intelligence techniques to find breakthroughs. My focus has been on not only accelerating the extraction of knowledge from intricate, multi-dimensional, and often noisy measurements but also on making data collection smarter. By integrating these cutting-edge technologies, I aim to transform how we approach material science and deepen our understanding of material behavior and device performance.
