Daphne Koller
Insitro, Inc.
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Primary Section: 34, Computer and Information Sciences Secondary Section: 29, Biophysics and Computational Biology Membership Type:
Member
(elected 2023)
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Biosketch
Daphne Koller is CEO and Founder of insitro, a machine learning-driven drug discovery and development company. She was the co-founder and co-CEO of online education platform Coursera, and is the co-founder of Engageli, an interactive digital learning platform. Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years, and where she remains an Adjunct Faculty member. She is the author of over 300 refereed publications spanning Computer Science and Life Science venues, with an h-index of over 145. She was recognized as one of TIME Magazine’s 100 most influential people in 2012 and Newsweek’s 10 most important people in 2010. She is the recipient of the Sloan Foundation Faculty Fellowship (1996), ONR Young Investigator Award (1998), Presidential Early Career Award for Scientists and Engineers (1999), IJCAI Computers and Thought Award (2001), MacArthur Foundation Fellowship (2004), ACM Prize in Computing (2008), the ACM AAAI Allen Newell Award (2019), IEEE CS Women of ENIAC Computer Pioneer award (2022), AnitaB.org Technical Leadership Abie Award Winner (2022). Daphne is a member of the National Academy of Science (2023), the National Academy of Engineering (2011), the International Society of Computational Biology (2017), the American Academy of Arts and Sciences (2014), and the American Association for Artificial Intelligence (2004).
Research Interests
Daphne Koller’s core interests lie in the development of novel methods in machine learning and their use to discern patterns in complex data sets, with a particular focus on biomedical applications. She has done extensive work on probabilistic graphical models, relational learning, weakly supervised learning, active learning, and reinforcement learning. Her current focus is on the use of representation learning methods to learn the language of biology as manifested in high-content data across different biological scales, spanning from cellular systems through human clinical data. She uses these techniques to help predict the clinical impact of interventions, towards the goal of bringing better medicines to the patients who will benefit the most.
