School of Engineering
Showing 401-500 of 2,288 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
Group Voice Collaborative Pianist, MusicBioSIEPR 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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Yiwen Dong
Postdoctoral Scholar, Computer Science
BioYiwen Dong is a postdoc fellow at the Stanford Institute of Human-Centered Artificial Intelligence (HAI). Her research interest is human behavior characterization and health monitoring through their interactions with the physical environment. Her current work focuses on human and animal health monitoring through gait-induced floor vibrations.
While buildings are traditionally considered as passive and indifferent, her works allow the buildings to be both self-aware and user-aware. Yiwen developed systems that utilize ambient structural vibrations to infer human behaviors and health status, which enables many smart building applications such as in-home patient monitoring and elder care, intruder prevention and occupant management, animal health monitoring, and welfare. She strives for the next-generation intelligent infrastructures by exploring the potential of structural monitoring for human-centered purposes.
Yiwen has an interdisciplinary background in civil engineering, electrical engineering, and AI. Yiwen received her Master’s degree in Structural Engineering at Stanford University and her Bachelor’s degree in civil engineering at Nanyang Technological University. She won various awards (Best Paper Award, runner-ups in competitions) in ubiquitous computing and cyber-physical system conferences. She is passionate about combining the physical knowledge from the living environments, sensing approaches from cyber-physical systems, and data-driven models from machine learning to infer people’s behavior patterns and health status. -
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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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 Internet security, trust, and safety. I build systems to measure complex networked ecosystems, and I use the resulting perspective to understand real-world behavior, uncover weaknesses and attacks, architect and deploy more resilient approaches, and guide public policy.
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Vijay Prakash Dwivedi
Postdoctoral Scholar, Computer Science
BioVijay Prakash Dwivedi is a Postdoctoral Scholar in Computer Science working on graph representation learning. He holds a PhD from Nanyang Technological University (NTU), Singapore. His work has made contributions to advancing benchmarks for Graph Neural Networks (GNNs), graph positional and structural encodings, and Graph Transformers as universal deep neural networks for graph-based learning. He has also contributed to the integration of parametric knowledge in large language models (LLMs) for diverse applications, particularly in healthcare. Several of the methods he developed during his PhD are now widely adopted in state-of-the-art Graph Transformers and other leading graph learning models. For his research, he received one of the Outstanding PhD Thesis Awards from the NTU College of Computing and Data Science. Vijay has over 7 years experience in both academia and industry with institutions including NTU, Snap Inc., Sony, and ASUS.
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Barbara Elizabeth Engelhardt
Professor (Research) of Biomedical Data Science and, by courtesy, of Statistics and of Computer Science
BioBarbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.
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Francis Engelmann
Postdoctoral Scholar, Computer Science
BioFrancis Engelmann is a PostDoc at Stanford university with Prof. Leonidas Guibas and Prof. Jeannette Bogh. Before that, he was a postdoctoral researcher at ETH Zurich collaborating with Prof. Marc Pollefeys and a visiting researcher at Google Zurich working with Federico Tombari. His current research focuses on computer vision and deep learning, particularly in the realm of 3D scene understanding. Prior to joining ETH Zurich, he obtained his Ph.D. from RWTH Aachen University under the guidance of Prof. Bastian Leibe, and interned at Google X in Munich, Google Research in Zurich, and Apple in California. Francis is a Fellow of the ETH AI Center, a member of the ELLIS Society, and a recipient of ETHZ Career Seed Award and SNSF Postdoc.Mobility fellowship.
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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
On Partial Leave from 10/01/2024 To 06/30/2025BioI 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.
