School of Humanities and Sciences
Showing 1-21 of 21 Results
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Percy Liang
Associate Professor of Computer Science, Senior Fellow at the Stanford Institute for HAI, and Associate Professor, by courtesy, of Statistics
BioPercy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
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Scott W Linderman
Assistant Professor of Statistics and, by courtesy, of Computer Science and of Electrical Engineering
BioScott is an Assistant Professor of Statistics and, by courtesy, Electrical Engineering and Computer Science at Stanford University. He is also an Institute Scholar in the Wu Tsai Neurosciences Institute and a member of Stanford Bio-X and the Stanford AI Lab. His lab works at the intersection of machine learning and computational neuroscience, developing statistical methods to analyze large scale neural data. Previously, Scott was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University, and he completed his PhD in Computer Science at Harvard University with Ryan Adams and Leslie Valiant. He obtained his undergraduate degree in Electrical and Computer Engineering from Cornell University and spent three years as a software engineer at Microsoft before graduate school.
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Qiao Liu
Postdoctoral Scholar, Statistics
BioI am currently a postdoctoral scholar at the Department of Statistics, Stanford University, advised by Prof. Wing Hung Wong (NAS member). Prior to that, I was a PhD student at Tsinghua University, where I spent two years at Stanford University, jointly advised by Prof. Wing Hung Wong. My research interests lie in the intersection of machine learning, statistics, and computational biology. I'm especially fascinated by solving several problems in statistics, such as density estimation, causal inference, and likelihood-free Bayesian, with deep generative models. Besides, I'm also interested in various problems in computational biology and biomedical informatics, which involve genomic data, pharmacology data, and biomedical data analysis.
