Stanford University
Showing 201-300 of 1,817 Results
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Kovid Capildeo
Undergraduate, Computer Science
BioI'm an undergrad from Trinidad exploring mathematics and computer science, and I enjoy robotics, contest math and calypso on the side.
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Edward Y. Chang
Affiliate, Computer Science
BioEdward Y. Chang has been an adjunct professor in Stanford’s Computer Science Department since 2019. Previously, he was a tenured professor at UC Santa Barbara. From 2006 to 2012, he served as a director at Google Research, where he pioneered data-centric and parallel machine learning and contributed to the ImageNet project. Chang later became president of HTC Healthcare, where he developed AI-powered diagnostics and won the Tricorder XPRIZE. He has also held positions at HKUST and UC Berkeley. Chang earned an MS in Computer Science and a PhD in Electrical Engineering from Stanford. He is a Fellow of ACM and IEEE for his contributions to scalable machine learning and healthcare AI.
Since 2019, Chang’s research has focused on virtual assistance, collaborating with Monica Lam, and more recently on large language models (LLMs). He hypothesizes that LLM Collaborative Intelligence (LCI) could pave the way toward artificial general intelligence (AGI).
Chang has authored seven books, including:
Unlocking the Wisdom of Large Language Models (2024)
LLM Collaborative Intelligence: The Path to Artificial General Intelligence (2024)
Journey of the Mind (Poetry, 2023)
Mandarin translation of Erwin Schrödinger’s What is Life? Mind and Matter (2021)
Big Data Analytics for Large-Scale Multimedia Search (2019)
Nomadic Eternity (Poetry, 2012)
Foundations of Large-Scale Multimedia Information Management and Retrieval (2011) -
Moses Charikar
Donald E. Knuth Professor
Current Research and Scholarly InterestsEfficient algorithmic techniques for processing, searching and indexing massive high-dimensional data sets; efficient algorithms for computational problems in high-dimensional statistics and optimization problems in machine learning; approximation algorithms for discrete optimization problems with provable guarantees; convex optimization approaches for non-convex combinatorial optimization problems; low-distortion embeddings of finite metric spaces.
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Jieneng Chen
Affiliate, Program-Wu, J.
BioJieneng Chen is a postdoctoral researcher at Stanford SVL, working with Prof. Jiajun Wu. He received the PhD in Computer Science at Johns Hopkins University in 2026. He is a recipient of Siebel Scholar award, Nvidia academic grant, multiple best paper awards, young american scientist award, and young investigator awards.
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Yinghan Chen
Undergraduate, Computer Science
BioI am currently working with The Movement Lab (TML) at the Department of Computer Science, advised by Dr. Karen Liu.
My research lies at the intersection of robot learning, physics-based simulation, grasping and manipulation, and multimodal perception. I am broadly interested in enabling embodied agents to understand physical structures, reason about dynamics, and perform dexterous manipulation through integrated multimodal sensing. My recent work spans visual–tactile sensing and learning, dexterous manipulation, tool-use and design, and differentiable simulation. I have published or submitted papers to top venues in robotics and embodied AI including RA-L, IROS and CoRL, and I hope to continue probing the deeper principles underlying intelligent robotic systems!
My long-term goal is to develop general-purpose robotic intelligence capable of perceiving, planning, and acting in the physical world with human-level adaptability and finesse, ultimately enabling robots to assist humans in everyday, unstructured environments.
For more details, please visit: www.yinghanchen.com -
Yejin Choi
Dieter Schwarz Foundation HAI Professor and Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence
BioYejin Choi is the Dieter Schwarz Foundation Professor and Senior Fellow at the Department of Computer Science at Stanford University and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) respectively. Choi is MacArthur Fellow (class of 2022), AI2050 Senior Fellow (class of 2024), and named among Time100 Most Influential People in AI in 2023. In addition, Choi is a co-recipient of 2 Test-of-Time awards and 8 Best and Outstanding Paper Awards at top AI conferences including ACL, ICML, NeurIPS, ICCV, CVPR, and AAAI, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI’s 10 to Watch in 2016. Choi was a main stage speaker at TED 2023, and a keynote speaker for a dozen conferences across several AI disciplines including ACL, CVPR, ICLR, MLSys, VLDB, WebConf, and AAAI. Her current research interests include fundamental limits and capabilities of large language models, alternative training recipes for language models, symbolic methods for neural networks, reasoning and knowledge discovery, moral norms and values, pluralistic alignment, and AI safety.
