School of Engineering
Showing 1-100 of 1,815 Results
-
Sara Achour
Assistant Professor of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsI am an Assistant Professor jointly appointed to both the Computer Science and the Electrical Engineering Departments at Stanford University. My research focuses on new techniques and tools, specifically new programming languages, compilers, and runtime systems, that enable end-users to more easily develop computations that exploit the potential of emerging computing platforms that exhibit analog behaviors.
-
Ehsan Adeli
Assistant Professor (Research) of Psychiatry and Behavioral Sciences (Public Mental Health and Populations Sciences) and, by courtesy, of Computer Science and of Biomedical Data Science
Current Research and Scholarly InterestsMy research lies in the intersection of Machine Learning, Computer Vision, Healthcare, Ambient Intelligence, and Computational Neuroscience.
-
Maneesh Agrawala
Forest Baskett Professor and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsComputer Graphics, Human Computer Interaction and Visualization.
-
Alex Aiken
Alcatel-Lucent Professor of Communications and Networking, Professor of Particle Physics and Astrophysics, and of Photon Science
BioAlex Aiken is the Alcatel-Lucent Professor of Computer Science at Stanford. Alex received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. Alex was a Research Staff Member at the IBM Almaden Research Center (1988-1993) and a Professor in the EECS department at UC Berkeley (1993-2003) before joining the Stanford faculty in 2003. His research interest is in areas related to programming languages.
-
Youssef Allouah
Postdoctoral Scholar, Computer Science
BioYoussef Allouah is a postdoctoral scholar at Stanford University. He is working with Prof. Sanmi Koyejo at the Computer Science department. His research interests are broadly in the principles of trustworthy machine learning, specifically the theory and practice of privacy, robustness, and unlearning.
-
Emily Alsentzer
Assistant Professor of Biomedical Data Science, of Medicine (Computational Medicine) and, by courtesy, of Computer Science
BioDr. Emily Alsentzer is an Assistant Professor in Biomedical Data Science and, by courtesy, Computer Science at Stanford University. Her research leverages machine learning (ML) and natural language processing (NLP) to augment clinical decision-making and broaden access to high quality healthcare. She focuses on integrating medical expertise into ML models to ensure responsible deployment in clinical workflows. Dr. Alsentzer completed a postdoctoral fellowship at Brigham and Women’s Hospital where she worked to deploy ML models within the Mass General Brigham healthcare system. She received her PhD from the Health Sciences and Technology program at MIT and Harvard Medical School and holds degrees in computer science (BS) and biomedical informatics (MS) from Stanford University. She has served as General Chair for the Machine Learning for Health Symposium and founding organizer for SAIL and the Conference on Health, Inference, and Learning (CHIL).
-
Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine, of Biomedical Data Science, Senior Fellow at the Stanford Institute for Human-Centered AI and Professor, by courtesy, of Computer Science
Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
-
Leni Aniva
Ph.D. Student in Computer Science, admitted Autumn 2022
Current Research and Scholarly InterestsMy main research interest is Machine-Assisted Theorem Proving, which refers to using machine-learning agents to find proofs of mathematical theorems and conduct reasoning. We are pursuing a hybrid algorithm between neural networks and SMT solvers to solve some of the long standing problems facing machine learning models today such as opaqueness, hallucinations, and information leakage. I also do research on SMT solvers and automatic verifications of programs.
