School of Engineering
Showing 51-72 of 72 Results
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Stefano Ermon
Assistant Professor of Computer Science and Center Fellow, by courtesy, at the Woods Institute for the Environment
BioI 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. -
Aslan Etminan
Postdoctoral Research Fellow, Electrical Engineering
BioAslan Etminan is a postdoctoral research fellow under the supervision of Prof. Amin Arbabian at Arbabian Lab in the Stanford University. He received his B.Sc. degree in electrical engineering from the Sharif University of Technology, Tehran, Iran, in 2011, M.Sc. degree in electrical and electronics engineering from the Bilkent University, Ankara, Turkey, in 2013, and Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles, California, in 2019. He also served for the Bilkent University Computational Electromagnetics Research Center (BiLCEM) in the Bilkent University (2011-2015) and Microwave Systems, Sensors and Imaging Lab (MiXiL) in the University of Southern California as a research assistant (2015-2019). His research interests are microwave imaging, inverse scattering problems, and remote sensing applications.
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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.
