Stanford University
Showing 391-400 of 425 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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Chunchen Xu
Postdoctoral Scholar, Psychology
BioI am currently a postdoc researcher at the Psychology Department at Stanford University. I study culture and the self in the context of AI-based smart technological developments. The first line of my work focuses on understanding and critiquing extant technological systems from a cultural perspective. I unpack cultural assumptions underlying conceptions of smart technology and examine technology's social and psychological impact. The second line of my work seeks to untether the self from extant mainstream meaning systems and open the space of the imaginary. I explore how historically marginalized cultural worldviews offer clues for diversifying conceptions of smart technology towards building a more equitable society and a caring ecology.
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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.
