Computer Science
Showing 441-460 of 2,145 Results
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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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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 am interested in how to protect people against attacks on the Internet ranging from cybercrime and harassment to censorship and disinformation. I am broadly an empiricist: I build systems to measure complex networked ecosystems at scale, which I use to understand real-world behavior, uncover weaknesses and attacks, architect more resilient defenses, 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.
