School of Engineering
Showing 501-600 of 2,321 Results
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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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Bruno Felisberto Martins Ribeiro
Visiting Associate Professor, Computer Science
BioBruno Ribeiro is an Associate Professor in the Department of Computer Science at Purdue University and currently a Visiting Associate Professor at Stanford University. Before joining Purdue, he earned his Ph.D. from the University of Massachusetts Amherst and was a postdoctoral fellow at Carnegie Mellon University. Ribeiro has made significant contributions in the intersection between invariant theory, graph learning, and out-of-distribution robustness. Ribeiro received an NSF CAREER award in 2020, an Amazon Research Award in 2022, and multiple best paper awards.
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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
On Partial Leave from 04/01/2024 To 06/30/2024BioChelsea 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 experience, 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, an NSF graduate fellowship, a Facebook fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, 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.
Website: https://ai.stanford.edu/~cbfinn -
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. -
Zipeng Fu
Ph.D. Student in Computer Science, admitted Autumn 2022
BioZipeng Fu is a CS PhD student at Stanford AI Lab, advised by Chelsea Finn. His research focuses on deployable robot systems and learning in the unstructured open world. His representative work includes Mobile ALOHA, Robot Parkour Learning, and RMA, receiving CoRL 2023 & 2022 Best System Finalist awards. His research is supported by Stanford Graduate Fellowship as a Pierre and Christine Lamond Fellow. Previously, he was a student researcher at Google DeepMind. He completed his master's at CMU and bachelor’s at UCLA. Homepage: https://zipengfu.github.io/
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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
Undergraduate, Computer Science
Student Employee, Ricker DiningBioImmersed 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:
http://gaodrew.com/
https://andrewgao.dev/ -
Aimee Garza
Program Coordinator, Computer Science
Current Role at StanfordCS DEI Program Coordinator
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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
BioI'm a first-year CS Ph.D. student at Stanford. I'm advised by Prof. Jiajun Wu and affiliated with the Stanford Vision and Learning Lab. My research lies at the intersection between Graphics, 3D Vision, and Machine Learning. Specifically, I'm currently interested in physical scene understanding by inverting graphics engines.
Previously, I got my bachelor's degree in Computer Science from Zhejiang University in 2023, with an honors degree from Chu Kochen Honors College. During my undergraduate, I was fortunate to work closely with Prof. Xiaowei Zhou, Prof. Sida Peng and Prof. Jiajun Wu on several research projects.
You can find more information on my homepage: https://chen-geng.com
