School of Engineering
Showing 21-30 of 137 Results
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David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
Research Statement:
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems. -
Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
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Eric Dunham
Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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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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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
On Partial Leave from 04/01/2024 To 06/30/2024Current 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.
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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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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. -
Oliver Fringer
Professor of Civil and Environmental Engineering and of Oceans
BioFringer's research focuses on the development and application of numerical models and high-performance computational techniques to the study of fundamental processes that influence the dynamics of the coastal ocean, rivers, lakes, and estuaries.
