School of Engineering
Showing 401-500 of 2,302 Results
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Dora Demszky
Assistant Professor of Education and, by courtesy, of Computer Science
BioDr. Demszky is an Assistant Professor in Education Data Science at the Graduate School of Education at Stanford University. She works on developing natural language processing methods to support equitable and student-centered instruction. She has developed tools to give feedback to teachers on dialogic instructional practices, to analyze representation in textbooks, measure the presence of dialect features in text, among others. Dr Demszky has received her PhD in Linguistics at Stanford University, supervised by Dr Dan Jurafsky. Prior to her PhD, Dr. Demszky received a BA summa cum laude from Princeton University in Linguistics with a minor in Computer Science.
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Abhijit Devalapura
Masters Student in Computer Science, admitted Autumn 2021
BioSIEPR Undergraduate Research Fellow 2022-2023
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David Dill
Donald E. Knuth Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsSecure and reliable blockchain technology at Facebook.
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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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John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
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David Durst
Ph.D. Student in Computer Science, admitted Autumn 2017
BioDavid is a Computer Science PhD candidate at Stanford University. He's advised by Kayvon Fatahalian and Pat Hanrahan and affiliated with the AHA Agile Hardware Center. His research focuses on programming languages and computer architecture. He's supported by an NSF Graduate Research Fellowship and a Stanford Graduate Fellowship in Science and Engineering. Previously, he worked at BlackRock as a Financial Modeling Group Analyst and received a B.S.E. in Computer Science from Princeton University in 2015.
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Zakir Durumeric
Assistant Professor of Computer Science
BioI am an Assistant Professor of Computer Science. My research brings a large-scale, empirical approach to the study of security, abuse, and misinformation on the Internet. I build systems to measure complex networked ecosystems, and I use the resulting perspective to understand real-world behavior, uncover weaknesses and attacks, architect more resilient approaches, and guide policy decisions.
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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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Stefano Ermon
Associate Professor of Computer Science and Senior Fellow 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. -
Chaofei Fan
Ph.D. Student in Computer Science, admitted Autumn 2020
BioI’m a Ph.D. student at Stanford unraveling the future of brain-computer interfaces to revolutionize communication.
