School of Engineering
Showing 51-100 of 180 Results
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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. -
Michele Ferretti
Ph.D. Student in Mechanical Engineering, admitted Winter 2024
Masters Student in Mechanical Engineering, admitted Spring 2024BioMS/PhD Student and Hypersonics Researcher in the High-Temperature Gasdynamics Laboratory (Hanson group).
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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 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 -
Michael Fischbach
Liu (Liao) Family Professor
Current Research and Scholarly InterestsThe microbiome carries out extraordinary feats of biology: it produces hundreds of molecules, many of which impact host physiology; modulates immune function potently and specifically; self-organizes biogeographically; and exhibits profound stability in the face of perturbations. Our lab studies the mechanisms of microbiome-host interactions. Our approach is based on two technologies we recently developed: a complex (119-member) defined gut community that serves as an analytically manageable but biologically relevant system for experimentation, and new genetic systems for common species from the microbiome. Using these systems, we investigate mechanisms at the community level and the strain level.
1) Community-level mechanisms. A typical gut microbiome consists of 200-250 bacterial species that span >6 orders of magnitude in relative abundance. As a system, these bacteria carry out extraordinary feats of metabolite consumption and production, elicit a variety of specific immune cell populations, self-organize geographically and metabolically, and exhibit profound resilience against a wide range of perturbations. Yet remarkably little is known about how the community functions as a system. We are exploring this by asking two broad questions: How do groups of organisms work together to influence immune function? What are the mechanisms that govern metabolism and ecology at the 100+ strain scale? Our goal is to learn rules that will enable us to design communities that solve specific therapeutic problems.
2) Strain-level mechanisms. Even though gut and skin colonists live in communities, individual strains can have an extraordinary impact on host biology. We focus on two broad (and partially overlapping) categories:
Immune modulation: Can we redirect colonist-specific T cells against an antigen of interest by expressing it on the surface of a bacterium? How do skin colonists induce high levels of Staphylococcus-specific antibodies in mice and humans?
Abundant microbiome-derived molecules: By constructing single-strain/single-gene knockouts in a complex defined community, we will ask: What are the effects of bacterially produced molecules on host metabolism and immunology? Can the molecular output of low-abundance organisms impact host physiology?
3) Cell and gene therapy. We have begun two new efforts in mammalian cell and gene therapies. First, we are developing methods that enable cell-type specific delivery of genome editing payloads in vivo. We are especially interested in delivery vehicles that are customizable and easy to manufacture. Second, we have begun a comprehensive genome mining effort with an emphasis on understudied or entirely novel enzyme systems with utility in mammalian genome editing. -
Martin Fischer
Kumagai Professor in the School of Engineering
BioProfessor Fischer's research goals are to improve the productivity of project teams involved in designing, building, and operating facilities and to enhance the sustainability of the built environment. His work develops the theoretical foundations and applications for virtual design and construction (VDC). VDC methods support the design of a facility and its delivery process and help reduce the costs and maximize the value over its lifecycle. His research has been used by many small and large industrial government organizations around the world.
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Ian Fisher
Professor of Applied Physics and, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsOur research focuses on the study of quantum materials with unconventional magnetic & electronic ground states & phase transitions. Emphasis on design and discovery of new materials. Recent focus on use of strain as a probe of, and tuning parameter for, a variety of electronic states. Interests include unconventional superconductivity, quantum phase transitions, nematicity, multipolar order, instabilities of low-dimensional materials and quantum magnetism.
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Aidan James Fitzpatrick
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioAIDAN FITZPATRICK received the B.S. degree in electrical and computer engineering from the University of Massachusetts Amherst, in 2018, and the M.S. degree in electrical engineering from Stanford University in 2020, where he is currently pursuing the Ph.D. degree in electrical engineering.
His current research interests are in computational imaging - specifically at the intersection of electromagnetics, acoustics, and signal processing for the codesign of imaging algorithms and system hardware for non-contact thermoacoustic/photoacoustic, and millimeter wave applications.
