Stanford University
Showing 1,901-1,950 of 2,085 Results
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Jiajun Wu
Assistant Professor of Computer Science and, by courtesy, of Psychology
BioJiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University, working on computer vision, machine learning, robotics, and computational cognitive science. Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. Wu's research has been recognized through the Young Investigator Programs (YIP) by ONR and by AFOSR, the NSF CAREER award, the Okawa research grant, the AI's 10 to Watch by IEEE Intelligent Systems, paper awards and finalists at ICCV, CVPR, SIGGRAPH Asia, ICRA, CoRL, and IROS, dissertation awards from ACM, AAAI, and MIT, the 2020 Samsung AI Researcher of the Year, and faculty research awards from Google, J.P. Morgan, Samsung, Amazon, and Meta.
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Min Wu
Postdoctoral Scholar, Computer Science
Current Research and Scholarly InterestsResponsible AI, AI safety, trustworthy AI, robustness, explainability and interpretability.
Formal methods, automated verification, verification of deep neural networks, formal explainable AI. -
Tiange Xiang
Ph.D. Student in Computer Science, admitted Autumn 2022
BioTiange Xiang is a Ph.D. student in Computer Science at Stanford University, where he is a member of the Stanford AI Lab (SAIL) and Stanford Vision and Learning Lab (SVL). His research interests include machine learning and computer vision in general. He received a bachelor's degree in Computer Science and Technology (Advanced)(Honors) from the University of Sydney, where he was awarded Honors Class I and the University Medal.
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Pei Xu
Postdoctoral Scholar, Computer Science
Current Research and Scholarly Interestscharacter animation, physics-based character control, crowd simulation
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Daniel Yamins
Associate Professor of Psychology and of Computer Science
Current Research and Scholarly InterestsOur lab's research lies at intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. It is founded on two mutually reinforcing hypotheses:
H1. By studying how the brain solves computational challenges, we can learn to build better artificial intelligence algorithms.
H2. Through improving artificial intelligence algorithms, we'll discover better models of how the brain works.
We investigate these hypotheses using techniques from computational modeling and artificial intelligence, high-throughput neurophysiology, functional brain imaging, behavioral psychophysics, and large-scale data analysis.
