Institute for Computational and Mathematical Engineering (ICME)
Showing 21-40 of 156 Results
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Léopold Cambier
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
Current Research and Scholarly InterestsFast Sparse Linear Solvers
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Emmanuel Candes
Barnum-Simons Chair in Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Gunnar Carlsson
Ann and Bill Swindells Professor, Emeritus
BioDr. Carlsson has been a professor of mathematics at Stanford University since 1991. In the last ten years, he has been involved in adapting topological techniques to data analysis, under NSF funding and as the lead PI on the DARPA “Topological Data Analysis” project from 2005 to 2010. He is the lead organizer of the ATMCS conferences, and serves as an editor of several Mathematics journals
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Ines Chami
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2016BioI am a second-year Masters student in the ICME data science program. Prior to joining Stanford, I studied mathematics and computer science at Ecole Centrale Paris. My research interests include computer vision, natural language processing and, more specifically, multimodal analysis. My previous research was focused on cross-modal information retrieval (image annotation and automated text-illustration). I am currently working on information extraction from semi-structured data (pdf tables) within the Hazy Research group led by Prof. Ré at Stanford.
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Casey Chu
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
Current Research and Scholarly InterestsTheoretical foundations of inference.
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Eric Darve
Professor of Mechanical Engineering
Current Research and Scholarly InterestsProfessor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, and machine learning with applications in engineering.
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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
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
Associate Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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Philip Etter
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
BioI'm is a third year PhD student in the Institute for Computational and Mathematical Engineering at Stanford University. My interests lie broadly in the realm of data science and computational mathematics, spanning machine learning, numerical linear algebra, theoretical computer science, and computational physics. In particular, my most recent research focuses on finding efficient methods to improve accuracy when solving linear systems with unstructured noise. My other research focuses on model order reduction, leveraging machine learning and linear algebra techniques to deliver massive performance boosts in many-query physics problems, e.g., Bayesian inference and uncertainty quantification, while simultaneously guaranteeing accurate results. I presented these techniques in talks at SIAM: CSE ’19 and at ICIAM ’19, and published in CMAME. In the past, I've also worked as a data science research intern at Sandia National Laboratories, a software engineering intern at Google, and a research contractor at Bell Labs.
I received my undergraduate degree from Princeton, where I studied mathematics, computer science, and physics. While I was there, I wrote my undergraduate thesis on numerical methods for solitonic boson star evolution and ground state searching, graduating summa cum laude. Before that, I did some research in theoretical optics. And before that, I was interested in graph algorithms. But while I have a very broad background in mathematics and related fields, I'm particularly excited by finding ways of using data to accelerate computation, build fast approximation techniques, and make predictions about the future (and inferences about the present).
Going forward, I want to continue to develop better and faster algorithms by bringing the power of data science to bear on interesting computational and statistical challenges.
My other assorted interests include quantum physics, general relativity, computer graphics, and music.
I prefer tabs to spaces, and vim to emacs. -
Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures, Professor of Mechanical Engineering and Director of the Army High Performance Computing Research Center
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.
