Wu Tsai Human Performance Alliance
Showing 1-10 of 15 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. Towards this end, we use a combination of approaches from cognitive science, computational neuroscience, and artificial intelligence.
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Richard E. Fan
Clinical Assistant Professor, Urology
BioRichard E. Fan, Ph.D., is an engineer embedded in the Department of Urology in the Stanford School of Medicine.
Dr. Fan’s research relates to the development of clinically driven biomedical instrumentation and medical devices. He is interested in translational application of emerging technologies in the medical and surgical spaces, as well as the development of platforms to explore clinical and pre-clinical evaluation. His primary work is currently focused on image guided detection and treatment of prostate cancer, including MR-US fusion, focal therapies, embedded systems and robotics. -
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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Vivian Feig
Assistant Professor of Mechanical Engineering and, by courtesy, of Materials Science and Engineering
BioThe Feig lab aims to develop low-cost, noninvasive, and widely-accessible medical technologies that integrate seamlessly with the human body. We accomplish this by developing functional materials and devices with dynamic mechanical properties, leveraging chemistry and physics insights to engineer novel systems at multiple length scales. In pursuit of our goals, we maintain a strong emphasis on integrity and diversity, while nurturing the intellectual curiosity and holistic growth of our team members as researchers, communicators, and leaders.
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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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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 and Senior Fellow at the Precourt Institute for Energy
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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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. -
Michael Fredericson, MD
Professor of Orthopaedic Surgery and, by courtesy, of Medicine (Stanford Prevention Research Center)
On Leave from 07/07/2025 To 08/06/2025Current Research and Scholarly InterestsMy research focuses on the etiology, prevention, and treatment of overuse sports injuries in athletes and lifestyle medicine practices for improved health and longevity.