Institute for Computational and Mathematical Engineering (ICME)
Showing 1-9 of 9 Results
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Eric Darve
Associate Professor of Mechanical Engineering
BioProfessor Darve's research is focused on the development of numerical methods for large scale scientific computing with applications in biomolecular simulations, acoustics, electromagnetics, and microfluidics. In these applications, the computational expense of simulating large and complex systems is very significant and in many instances beyond current computer capabilities. He is developing innovative numerical techniques to reduce this computational expense and enable the simulation of complex systems over realistic time scales. Professor Darve also uses processors with novel architectures, such as GPUs and the Cell processor, for scientific computing. Applications range from particle simulation to fluid dynamics and solving partial differential equations.
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Andrew Deveau
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2017
BioI am a masters student in the data science track within the Institute for Computational and Mathematical Engineering. Before coming to Stanford, I received a B.S. in mathematics from Yale and spent two years working for a proprietary trading firm. I am broadly interested in reinforcement learning and socially beneficial applications of machine learning.
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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
Associate Professor of Computer Science and, by courtesy, of Molecular and Cellular Physiology and of Structural Biology
BioRon Dror is an Associate Professor of Computer Science and, by courtesy, Molecular and Cellular Physiology and Structural Biology at Stanford University, where he is also affiliated with the Institute for Computational and Mathematical Engineering, the Stanford Artificial Intelligence Lab, Bio-X, ChEM-H, and the Biophysics and Biomedical Informatics Programs. Dr. Dror's research at Stanford addresses a broad set of computational biology problems related to the spatial organization and dynamics of biomolecules and cells.
Before joining Stanford in March 2014, Dr. Dror served as second-in-command of D. E. Shaw Research, a hundred-person company, having joined in 2002 as its first hire. At DESRES, he focused on high-performance computing and biomolecular simulation—in particular, developing technology that accelerates molecular dynamics simulations by orders of magnitude, and applying these simulations to the study of protein function, protein folding, and protein-drug interactions (part of a project highlighted by Science as one of the top 10 scientific breakthroughs of 2010).
Dr. Dror earned a PhD in Electrical Engineering and Computer Science at MIT, an MPhil in Biological Sciences as a Churchill Scholar at the University of Cambridge, and both a BA in Mathematics and a BS in Electrical and Computer Engineering at Rice University, summa cum laude. As a student, he worked in genomics, vision, image analysis, and neuroscience. He has been awarded a Fulbright Scholarship and fellowships from the National Science Foundation, the Department of Defense, and the Whitaker Foundation, as well as a Gordon Bell Prize and several Best Paper awards. -
Eric Dunham
Associate Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
