School of Engineering
Showing 1-20 of 33 Results
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Jonathan Fan
Associate Professor of Electrical Engineering
Current Research and Scholarly InterestsOptical engineering plays a major role in imaging, communications, energy harvesting, and quantum technologies. We are exploring the next frontier of optical engineering on three fronts. The first is new materials development in the growth of crystalline plasmonic materials and assembly of nanomaterials. The second is novel methods for nanofabrication. The third is new inverse design concepts based on optimization and machine learning.
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Shanhui Fan
Joseph and Hon Mai Goodman Professor of the School of Engineering and, Professor, by courtesy, of Applied Physics
BioFan's research involves the theory and simulations of photonic and solid-state materials and devices; photonic crystals; nano-scale photonic devices and plasmonics; quantum optics; computational electromagnetics; parallel scientific computing.
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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures, James and Anna Marie Spilker Chair of the Department of Aeronautics and Astronautics and Professor of Mechanical Engineering and of Aeronautics and Astronautics
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design and analysis of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on the nonlinear aeroelasticity and flight dynamics of Micro Aerial Vehicles (MAVs) with flexible flapping wings and N+3 aircraft with High Aspect Ratio (HAR) wings, layout optimization and additive manufacturing of wing structures, supersonic inflatable aerodynamic decelerators for Mars landing, and the reliable automated carrier landing via model predictive control. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-physics problems, high-order embedded boundary methods, uncertainty quantification, probabilistic machine learning, and efficient projection-based model order reduction as well as other forms of physics-based machine learning for time-critical applications such as design, active control, and digital twins.
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Aurore Fass
Visiting Asst Prof
BioI am a Visiting Assistant Professor of Computer Science at Stanford University. My research broadly focusses on Web security and privacy, Web measurements, and machine learning. Specifically, I am interested in detecting malware & vulnerabilities on the Web and collecting data to better understand and improve user security and privacy.
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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, rendering systems for machine learning, and the analysis of images and videos at scale.
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Ron Fedkiw
Professor of Computer Science
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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Jeffrey A. Feinstein, MD, MPH
Dunlevie Family Professor of Pulmonary Vascular Disease and Professor, by courtesy, of Bioengineering
Current Research and Scholarly InterestsResearch interests include (1) computer simulation and modeling of cardiovascular physiology with specific attention paid to congenital heart disease and its treatment, (2) the evaluation and treatment of pulmonary hypertension/pulmonary vascular diseases, and (3) development and testing of medical devices/therapies for the treatment of congenital heart disease and pulmonary vascular diseases.
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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
Associate Professor of Bioengineering and of Medicine (Microbiology and Immunology)
Current Research and Scholarly InterestsThe human microbiome is linked to a range of phenotypes in the host, but it remains difficult to test causality and explore the mechanisms of these interactions. Our lab focuses on two research areas that share a common goal of studying host-microbiota interactions at the level of molecular mechanism:
1) Technology development. Much of what we know about biology has been learned by deleting individual genes from mice, worms, flies and yeast. The ability to do single-strain and single-gene deletion in the microbiome would be transformative but does not yet exist. We are developing technology in three areas to make this possible:
Synthetic ecology: There is a pressing need for model systems for the microbiome that are defined, but of an order of complexity that approaches the native state. Key experiments in the field often show that a host phenotype can be transferred to a germ-free mouse via fecal transplant. If these phenomena could be recapitulated with a defined, high-complexity community, then reductionist experiments to probe mechanism would be possible. We are developing the technology required to build highly complex defined communities (100-200 bacterial species), make them stable upon transplantation into mice, and probe their function in vitro and in vivo.
Genetics: It is difficult to probe mechanism without genetics, and genetic tools exist for only ~10% of the bacterial species in the gut and skin microbiome. We are developing technologies that will make it possible to delete and insert genes, and build mutant libraries, in many of the most common bacterial strains in the gut and skin microbiome.
Computation: In previous work from the lab, we have developed computational algorithms that identify small-molecule-producing genes in bacterial genomes. In current work, we are devising algorithms for genome mining that are specific to the microbiome, and new tools for predicting the chemical structures of small molecules from untargeted metabolomics data.
2) Molecular mechanisms. Many of the early findings in microbiome research are correlative or associative. We are applying the tools described above to explore the mechanisms underlying these phenomena:
Small molecules: Our lab has had a long-standing interest in small molecules from the microbiota. These include: 1) host-derived molecules metabolized by the microbiome, like bile acids; 2) characteristic components of the bacterial membrane and cell wall, including LPS and capsular polysaccharides; and 3) hundreds of other diffusible small molecules (e.g., the products of polysaccharide and amino acid metabolism) that are present in the bloodstream at high concentrations. Our work in this area seeks to establish the mechanisms by which these molecules modulate host biology, especially by deleting them one at a time in the background of a complex community; and to discover new microbiome-derived metabolites present in the bloodstream and host tissues.
Ecology of complex communities: Synthetic ecology at the 100+ strain scale is entirely unexplored, and the emergent properties of complex communities are not well understood. Our work in this area seeks to understand basic principles outlined by the following questions: How many meaningful interactions exist in a community of hundreds of strains? What constitutes a niche, molecularly and spatially, and how do strains map to niches? What are the molecular correlates of stability, and how does a community reconfigure in response to a perturbation? How rare or common are stable states, and how predictable is the process by which a consortium will evolve toward a stable state? To what extent do priority effects (early colonists and events) determine the outcome of ecosystem development? Can the results of ecosystem competition be predicted or engineered? -
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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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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Sarah Fletcher
Assistant Professor of Civil and Environmental Engineering and Center Fellow at the Woods Institute for the Environment
Current Research and Scholarly InterestsThe Fletcher Lab aims to advance water resources management to promote resilient and equitable responses to a changing world.
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June Flora
Sr. Research Scholar
BioJune A. Flora, PhD, is a senior research scientist at Stanford University’s Human Sciences & Technologies Advanced Research Institute (HSTAR) in the Graduate School of Education, and the Solutions Science Lab in the Stanford School of Medicine. June's research focuses on understanding the drivers of human behavior change and the potential of communication interventions. The research is solution focused on behavior change relevant to health and climate change.
Most recently she is studying the role of energy use feedback delivered through motivationally framed online applications; the potential of children and youth delivered energy reduction interventions to motivate parent behavior change, and the effects of entertainment-education interventions to change behavior.
June earned her Ph.D. from Arizona State University in educational psychology. She has held faculty positions at University of Utah and Stanford University. -
Sean Follmer
Assistant Professor of Mechanical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsHuman Computer Interaction, Haptics, Robotics, Human Centered Design
