Stanford University
Showing 171-180 of 683 Results
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Barbara Elizabeth Engelhardt
Professor (Research) of Biomedical Data Science and, by courtesy, of Statistics and of Computer Science
BioBarbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.
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Dawson Engler
Associate Professor of Computer Science and of Electrical Engineering
BioEngler's research focuses both on building interesting software systems and on discovering and exploring the underlying principles of all systems.
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Daniel Bruce Ennis
Professor of Radiology (Veterans Affairs) and, by courtesy, of Bioengineering
Current Research and Scholarly InterestsThe Cardiac MRI Group seeks to invent and validate methods to quantify cardiac performance. We develop methods to measure cardiac structure (DWI/DTI), function (tagging and DENSE), flow (PC-MRI), and remodeling (diffusion, T1-mapping, fat-water mapping) for pediatrics and adults.
Fundamental to our research is a set of tools for numerically optimizing gradient waveforms, Bloch simulations, and patient-specific 3D-printed cardiovascular structures connected to computer controlled flow pumps. -
Anton Ermakov
Assistant Professor of Aeronautics and Astronautics and, by courtesy, of Geophysics and of Earth and Planetary Sciences
Current Research and Scholarly InterestsI am interested in the formation and evolution of the Solar System bodies and the ways we can constrain planetary interiors by geophysical measurements.
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Stefano Ermon
Associate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment
On Partial Leave from 10/01/2025 To 06/30/2026BioI am an Assistant Professor in the Department of Computer Science at Stanford University, where I am affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment.
My research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. -
Jonathan Fan
Associate Professor of Electrical Engineering and Senior Fellow at the Precourt Institute for Energy
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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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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Shanhui Fan
Joseph and Hon Mai Goodman Professor of the School of Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Applied Physics
BioFan's research interests are in fundamental studies of nanophotonic structures, especially photonic crystals and meta-materials, and applications of these structures in energy and information technology applications
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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
On Partial Leave from 04/01/2026 To 06/30/2026Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design, analysis, and digital twinning 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 reliable autonomous carrier landing in rough seas; dissipation of vertical landing energies through structural flexibility; nonlinear aeroelasticity of N+3 aircraft with High Aspect Ratio (HAR) wings; pulsation and flutter of a parachute; pendulum motion in main parachute clusters; coupled fluid-structure interaction (FSI) in supersonic inflatable aerodynamic decelerators for Mars landing; flight dynamics of hypersonic systems and their trajectories; and advanced digital twinning. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-component, multi-physics problems; discrete-event-free embedded boundary methods for CFD and FSI; efficient Bayesian optimization using physics-based surrogate models; modeling and quantifying model-form uncertainty; probabilistic, physics-based machine learning; mechanics-informed artificial neural networks for data-driven constitutive modeling; and efficient nonlinear projection-based model order reduction for time-critical applications such as design, active control, and digital twinning.
