Directory

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.

Slayton's research and teaching examine the relationships between and among risk, governance, and expertise, with a focus on international security and cooperation since World War II. Her first book, Arguments that Count: Physics, Computing, and Missile Defense, 1949-2012 (MIT Press, 2013), shows how the rise of a new field of expertise in computing reshaped public policies and perceptions about the risks of missile defense in the United States. In 2015, Arguments that Count won the Computer History Museum Prize.

Slayton's second book project, Shadowing Cybersecurity, examines the emergence of cybersecurity expertise through the interplay of innovation and repair. Slayton is also working on a third project which examines tensions intrinsic to the creation of a “smartâ  electrical power grid—i.e. a more sustainable, reliable, and secure grid. Both of these current projects are supported by a five-year National Science Foundation CAREER award, “Enacting Cybersecurity Expertise.â€Slayton is also a project lead on research funded by a Department of Homeland Security Center of Excellence, the Critical Infrastructure Resilience Institute.
 

Melissa Smith is a teaching professor in the Department of Statistics and Data Science. She joined the faculty in January 2013. She received an A.B. in mathematics from Dartmouth College and completed a Ph.D. in biostatistics at the University of North Carolina at Chapel Hill in 1996. Her previous positions were at the Indiana University School of Medicine in the Department of Biostatistics, the Marion County Health Department (Indianapolis), and the Cornell Statistical Consulting Unit (CSCU). She works in applied statistics on problems in medicine, public health, and health policy.

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.

Benjamin Soltoff is an associate teaching professor in information science. He teaches courses on data science, machine learning, research design, and data communication, and enjoys working with individuals from all backgrounds interested in applying computational methods to extract knowledge and inferences from a wide range of domains.

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.

Andrea Stevenson Won directs the Virtual Embodiment Lab. The lab's research focuses on how mediated experiences change people’s perceptions, especially in immersive media. Research areas include the clinical applications of virtual reality, and how nonverbal behavior as rendered in virtual environments affects collaboration and teamwork. Andrea Stevenson Won completed her PhD in the Virtual Human Interaction Lab in the Department of Communication at Stanford University. She also holds an MS in Biomedical Visualization from the University of Illinois at Chicago.
 

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.

Daniel Susser is an associate professor of information science. His research focuses on ethics, politics, and policy in computing. A philosopher by training, he studies governance problems raised by new and emerging data-driven technologies and the underlying conceptual and normative problems that make it difficult to understand and tackle them. He has broad interests in technology ethics and policy, philosophy of technology, and science and technology studies, especially critical questions about data, privacy, and the ethics of automation.

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.