Department Directory

David Shmoys obtained his Ph.D. in Computer Science from the University of California at Berkeley in 1984, and held postdoctoral positions at MSRI in Berkeley and Harvard University, and a faculty position at MIT before joining the Cornell faculty. He is Co-Chair of the Academic Planning Committee for Cornell Tech and Associate Director of the Institute of Computational Sustainability at Cornell University.

He is a Fellow of the ACM, INFORMS, and of SIAM, was an NSF Presidential Young Investigator, and has served on numerous editorial boards, including Mathematics of Operations Research (for which he is currently an Associate Editor), Operations Research, ORSA Journal on Computing, Mathematical Programming, and the SIAM Journals of both Computing and Discrete Mathematics, where for the latter he also served as Editor-in-Chief. He has been the advisor for 21 graduated Ph.D. students, and his former students are currently on the faculties of many leading universities and research labs, including MIT, Waterloo, Brown, Maryland, Georgetown, and D-Wave.

Shmoys' research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, and most recently, comptuational sustainability. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems; his recent book, co-authored with David Williamson), The Design of Approximation Algorithms, was awarded the 2013 Lanchester Prize by INFORMS.
 

Alexandra Silva is a professor in the Department of Computer Science at Cornell University. Before joining the faculty at Cornell, she was a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at the Programming Principles, Logic and Verification Group, University College London.

Rachee Singh is an assistant professor of computer science at Cornell University where she leads the sysphotonics group. Her group develops systems and algorithms for efficient communication on server-scale, rack-scale, and long-haul photonic interconnects. We use these systems to improve the performance of important applications like distributed machine learning and general planet-scale cloud workloads.

Noah Snavely is a professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. Snavely’s research interests are in computer vision and graphics, in particular, recovering structure from large photo collections for use in understanding and visualizing the world around us.

Nicholas Spooner is an assistant professor of computer science at Cornell University and a member of the Theory of Computing group. His work focuses on post-quantum proof systems. His interests include probabilistic proof systems and zero knowledge in general, post-quantum cryptography, quantum information, coding theory, and computational complexity. 

Karthik Sridharan is an associate professor in the Department of Computer Science at Cornell University. Prior to that, he was a postdoctoral research associate at the University of Pennsylvania, jointly with Alexander Rakhlin and Michael Kearns. He obtained his Ph.D. from Toyota Technological Institute at Chicago where his thesis advisor was Nathan Srebro. His primary area of research is theoretical machine learning, with interests in statistical learning theory, online learning, optimization, empirical process theory, concentration inequalities, and game theory.

Noah Stephens-Davidowitz is an assistant professor in the Department of Computer Science, and a member of the applied math field and the mathematics field. His research to date has focused primarily on the study of lattices. He is also interested more broadly in theoretical computer science, cryptography, and geometry.

Jennifer J. Sun is an assistant professor in the Department of Computer Science at Cornell University. Her research develops AI for scientists to accelerate discovery across fields. Her group works with researchers and domain experts to build approaches that enable new ways to collaborate between human experts and machine learning systems. Her work intersects different areas of computer vision and machine learning, including representation learning, program synthesis, generative modeling, and human-AI collaboration. Sun received her Ph.D. in computing and mathematical science from the California Institute of Technology and her bachelor's degree in engineering science (electrical and computer engineering) from the University of Toronto.

Wen Sun is an assistant professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. His research focuses on machine learning and artificial intelligence. Sun’s group at Cornell works on reinforcement learning and its applications to generative AI and robotics.

Éva Tardos is the Jacob Gould Schurman Professor of Computer Science. Her research interests include algorithms and algorithmic game theory, the subarea of theoretical computer science theory of designing and analyzing systems, and algorithms for selfish users. Her research focuses on algorithms and games on networks and simple auctions. She is most known for her work on quantifying the efficiency of selfish routing. Tardos received her Dipl.Math. and Ph.D. from Eötvös University, in Hungary. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences. She is the recipient of a number of fellowships and awards including the Packard Fellowship and the IEEE von Neumann Medal.

Angelique Taylor is the Andrew H. and Ann R. Tisch Assistant Professor at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science.

Tim Teitelbaum's early research focus was on Integrated Development Environments (IDEs), Syntax-Directed Editing, and Incremental Computation. His later work was on Automated Program Analysis for cybersecurity.

John Thickstun is an assistant professor of computer science. He works on machine learning and generative models. Thickstun is interested in methods that control the behavior of models, both from the perspective of a user who hopes to use a model to accomplish concrete tasks, and from the perspective of a model provider or policymaker who hopes to broadly regulate the outputs of a model. He is also interested in applications of generative models that push beyond the standard text and image modalities, including music technologies.

Immanuel Trummer is an associate professor of computer science. In his research, he makes data analysis more efficient and more user-friendly, often leveraging methods from the area of machine learning. His papers were selected for “Best of VLDB,” “Best of SIGMOD,” the ACM SIGMOD Research Highlight Award, and for publication in CACM as a CACM Research Highlight. He is a recipient of the NSF CAREER Award and multiple Google Faculty Research Awards. Trummer obtained a Ph.D. in computer science from the École Polytechnique Fédérale de Lausanne and a French-German double diploma in computer science and engineering.

Professor Emeritus; Computer Science; Ph.D., Univ of Michigan, 1973

Robbert van Renesse is a professor in the Department of Computer Science at Cornell University. He is a member of the Systems and Networking group and is interested in distributed systems, particularly in their  fault tolerance and scalability aspects. He is co-editor-in-chief for ACM Transactions on Computing Systems. He also plays jazz guitar and banjo in various bands, including the Ageless Jazz Band, the The JazzHappensBand, and Soda Ash Six. Van Renesse received his M.Sc. in mathematics and computer science and his Ph.D. in computer science, both from the Vrije Universiteit under the supervision of Andrew S. Tanenbaum. He is a Fellow of the Association of Computing Machinery.

Anke van Zuylen is a teaching professor in the Department of Computer Science. Her research interests are in combinatorial optimization. She designs and analyzes algorithms for optimization problems arising in areas such as: information science, in particular, aggregating inconsistent structured information; network design, scheduling and logistics, and computational biology.