School of Engineering
Showing 401-500 of 1,781 Results
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Judith Ellen Fan
Assistant Professor of Psychology, by courtesy, of Education and of Computer Science
BioI direct the Cognitive Tools Lab (https://cogtoolslab.github.io/) at Stanford University. Our lab aims to reverse engineer the human cognitive toolkit—in particular, how people use physical representations of thought to learn, communicate, and solve problems. Toward this end, we use a combination of approaches from cognitive science, computational neuroscience, and artificial intelligence to achieve deeper understanding of quintessentially human ways of thinking and imagining. Our broader goal is to leverage such scientific understanding of human cognition to guide the development of technologies that augment human agency and creativity.
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Haoquan Fang
Ph.D. Student in Computer Science, admitted Autumn 2026
BioHaoquan is a CS PhD student at Stanford University, advised by Prof Fei-Fei Li as part of the Stanford Vision and Learning Lab. Currently, he is also a research intern at NVIDIA Cosmos Lab, working with VP Ming-Yu Liu and Dr Haotian Zhang.
Previously, he has spent time at Ai2 PRIOR and Ai2 Robotics. He obtained his BS degree from the University of Washington, where he double majored in computer science (with honors) and statistics, and minored in mathematics. He was advised by Prof Ranjay Krishna, Prof Ali Farhadi, Prof Dieter Fox, and Prof Jenq-Neng Hwang. He was also mentored by and collaborated with Prof Jiafei Duan and Dr Ying Jin.
His research interests lie broadly in robot learning. In particular, he focuses on developing foundation models for robotic manipulation that are deployable in the real world and unlock novel capabilities. -
Kayvon Fatahalian
Associate Professor of Computer Science
BioKayvon Fatahalian is an Associate Professor in the Computer Science Department at Stanford University. Kayvon's research focuses on the design of systems for real-time graphics, high-efficiency simulation engines for applications in entertainment and AI, and platforms for the analysis of images and videos at scale.
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Ron Fedkiw
Canon Professor in the School of Engineering
BioFedkiw's research is focused on the design of new computational algorithms for a variety of applications including computational fluid dynamics, computer graphics, and biomechanics.
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Steven Feng
Ph.D. Student in Computer Science, admitted Autumn 2022
BioI'm a Stanford Computer Science PhD student and NSERC PGS-D scholar, working with the Stanford AI Lab and Stanford NLP Group. I am co-advised by Michael C. Frank and Noah Goodman as part of the Language & Cognition (LangCog) and Computation & Cognition (CoCo) Labs. I am grateful to receive support from Amazon Science, Microsoft AFMR, and StabilityAI.
My ultimate goal is to blend knowledge from multiple disciplines to advance AI research. My current research centers around aligning foundation model and human learning and capabilities, particularly in reasoning, generalization, and efficiency. I have explored ways to improve the controllability of language and visual generation models, and integrate structured and multimodal information to enhance their reasoning capabilities.
I'm investigating psychologically and cognitively inspired methods for continual learning, self-improvement, and advanced reasoning in foundation models. I'm also exploring methods to bridge the data efficiency gap between human and model learning while shedding further light on human cognitive models and our efficient language and vision acquisition capabilities.
Previously, I was a master's student at Carnegie Mellon University (CMU), where I worked with Eduard Hovy and Malihe Alikhani on language generation, data augmentation, and commonsense reasoning. Before that, I was an undergraduate student at the University of Waterloo, where I worked with Jesse Hoey on dialogue agents and text generation.
My research contributions have been recognized with several publications at major conferences and a best paper award at INLG 2021. I am also an Honorable Mention for the Jessie W.H. Zou Memorial Award and CRA Outstanding Undergraduate Researcher Award.
I am a co-instructor for the Stanford CS25 Transformers course, and mentor and advise several students. I also led the organization of CtrlGen, a controllable generation workshop at NeurIPS 2021, and was involved in the GEM benchmark and workshop for NLG evaluation.
In my free time, I enjoy gaming, playing the piano and guitar, martial arts, and table tennis. I am also the founder and president of the Stanford Piano Society. -
Richard Fikes
Professor (Research) of Computer Science, Emeritus
BioRichard Fikes has a long and distinguished record as an innovative leader in the development of techniques for effectively representing and using knowledge in computer systems. He is best known as co-developer of the STRIPS automatic planning system, KIF (Knowledge Interchange Format), the Ontolingua ontology representation language and Web-based ontology development environment, the OKBC (Open Knowledge Base Connectivity) API for knowledge servers, and IntelliCorp's KEE system. At Stanford, he led projects focused on developing large-scale distributed repositories of computer-interpretable knowledge, collaborative development of multi-use ontologies, enabling technology for the Semantic Web, reasoning methods applicable to large-scale knowledge bases, and knowledge-based technology for intelligence analysts. He was principal investigator of major projects for multiple Federal Government agencies including the Defense Advanced Research Projects Agency (DARPA) and the Intelligence Community’s Advanced Research and Development Activity (ARDA).
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Chelsea Finn
Assistant Professor of Computer Science and of Electrical Engineering
BioChelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, and the William George and Ida Mary Hoover Faculty Fellow. Professor Finn's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction. Her work lies at the intersection of machine learning and robotic control, including topics such as end-to-end learning of visual perception and robotic manipulation skills, deep reinforcement learning of general skills from autonomously collected data, and meta-learning algorithms that can enable fast learning of new concepts and behaviors. Professor Finn received her Bachelors degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Presidential Early Career Award for Scientists and Engineers, and the MIT Technology Review 35 under 35 list, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across three universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.
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Sean Follmer
Associate Professor of Mechanical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsHuman Computer Interaction, Haptics, Robotics, Human Centered Design
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Emily Fox
Professor of Statistics and of Computer Science
BioEmily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University. Prior to Stanford, she was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer. Before joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.
Emily has been awarded a CZ Biohub Investigator Award, Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in modeling complex time series arising in health, particularly from health wearables and neuroimaging modalities. -
Cristian Galdeano
Robotics Researcher, Robotics Center
BioCristian Galdeano is a Robotics Researcher at the Stanford Robotics Center. He received his M.S. in Electrical Engineering from Stanford University, where he focused on robotics, perception, and sensing. His work involves robotic perception, vision-guided manipulation, and autonomous systems.
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Swapnil Gandhi
Ph.D. Student in Computer Science, admitted Autumn 2022
BioMy broad research interests include distributed systems and cloud computing – in particular, I am interested in the system-side problems associated with learning, deploying, and operationalizing machine learning models at scale.
Previously, I was a Research Fellow at Microsoft Research India and prior to that obtained my Masters (by Research) in Computer and Data Systems from the Indian Institute of Science (IISc). -
Andrew Kean Gao
Masters Student in Computer Science, admitted Autumn 2023
BioImmersed in the AI space since 2019, Andrew is excited by the potential of AI/ML in all domains of industry, academia, and life. He has built several popular projects in AI, such as Lightspeed Multithreading and BibleGPT. His team won a Grand Prize at Stanford TreeHacks 2023 out of nearly 2,000 competitors. Beyond AI, Andrew has conducted research in molecular biology, disease diagnosis, drug design, and computational immunology.
Software developer and student at Stanford University specializing in artificial intelligence and large language models.
Personal websites:
https://andrew.md/
https://andrewgao.dev/ -
Chongkai Gao
Graduate Visiting Researcher Student, Computer Science
BioChongkai is a PhD student from the National University of Singapore, and a visiting student researcher at Stanford University in Prof. Fei-Fei Li's group. His research is about building hierarchical foundation models and structured evaluation of general-purpose robot manipulation. Homepage: https://chongkaigao.com/.
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Aimee Garza
Faculty Administrator, Computer Science
Current Role at StanfordCS DEI Program Coordinator
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Danielle Juliane Gaspar
Student Services Officer 2, Computer Science
Current Role at StanfordStudent Services Officer 2, Computer Science Department
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Michael Genesereth
Associate Professor of Computer Science
BioGenesereth is most known for his work on Computational Logic and applications of that work in Enterprise Management, Computational Law, and General Game Playing. He is one of the founders of Teknowledge, CommerceNet, Mergent Systems, and Symbium. Genesereth is the director of the Logic Group at Stanford and the founder and research director of CodeX - the Stanford Center for Legal Informatics.
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Chen Geng
Ph.D. Student in Computer Science, admitted Autumn 2023
Masters Student in Computer Science, admitted Autumn 2026BioI'm a CS Ph.D. student at Stanford, advised by Prof. Jiajun Wu. My research lies at the intersection between Graphics, 3D Vision, and Machine Learning. Previously, I got my bachelor's degree in Computer Science at the School of Computer Science and Chu Kochen Honors College, Zhejiang University. During my undergraduate, I was fortunate to work closely with Prof. Xiaowei Zhou and Prof. Jiajun Wu on several research projects.
You can find more information on my homepage: https://chen-geng.com -
Ashish Goel
Stanford W. Ascherman, MD Professor in the School of Engineering and Professor, by courtesy, of Computer Science
BioAshish Goel is a Professor of Management Science and Engineering, the Fortinet Founders Chair of Management Science and Engineering, and Professor (by courtesy) of Computer Science at Stanford University. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms.
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Siya Goel
Masters Student in Computer Science, admitted Autumn 2023
BioClass of 2026
Computer Science Major
Biology Minor -
Jason Goldberg
Ph.D. Student in Computer Science, admitted Autumn 2023
BioStanford Advisor # Position
Noah Goodman # Doctoral (Program)
John Mitchell # Doctoral Dissertation Co-Advisor (AC)
email: jasonmg@stanford.edu -
Christopher Gregg
Associate Professor (Teaching) of Computer Science
BioChris Gregg received his Ph.D. in Computer Engineering from the University of Virginia in 2012, has a Master's of Education from Harvard University (2002), and a BS in Electrical Engineering from Johns Hopkins University (1994). Prior to becoming a lecturer at Stanford, Chris was a lecturer in the computer science department at Tufts University, and prior to that he taught high school physics in Massachusetts and California for seven years. Chris was on active duty in the Navy for seven years, and remains as a Commander in the Navy Reserves in the Information Warfare / Cryptology community.
Chris's research interests include computer architecture (specifically, general purpose computing on GPUs) and the pedagogy of computer science teaching and instruction. -
Kalanit Grill-Spector
Susan S. and William H. Hindle Professor in the School of Humanities and Sciences and Professor, by courtesy, of Computer Science
Current Research and Scholarly InterestsFor humans, recognition is a natural, effortless skill that occurs within a few hundreds of milliseconds, yet it is one of the least understood aspects of visual perception. Our research utilizes functional imaging (fMRI),diffusion weighted imaging (DWI), computational techniques, and behavioral methods to investigate the neural mechanisms underlying visual recognition in humans. We also examine the development of these mechanisms from childhood to adulthood as well as between populations.
