Directory

M. Elizabeth Karns is a lawyer and epidemiologist. Her teaching is directly connected to her practice, which has focused on statistical evidence, occupational illnesses, assault and harassment injuries, and reproductive harms. Her current work on data science ethics brings together professional practice norms, individual values, and organizational goals. 

Kengo Kato is a professor of statistics and data science. His research fields are mathematical statistics, econometrics, and economic statistics with a focus on high dimensional statistical and econometric models. In 2020, Kato was awarded the Japan Academy Medal, regarded as the most prestigious award for young Japanese researchers. Among his many awards and honors, he is also a fellow of the Institute of Mathematical Statistics (IMS) and the recipient of the Analysis Award from the Mathematical Society of Japan (2021). He is the editor-in-chief of Bernoulli and the associate editor of the Japanese Economic Review and the Journal of Statistical Planning and Inference.

Jaehee Kim's research interests are in the general fields of population-genetic dynamical systems, statistical genetics, and mathematical phylogenetics. She also applies mechanistic understanding of population genetics and evolutionary biology to solve important questions in mathematical epidemiology and forensic genetics.
Education
Ph.D. in Physics, Stanford University
B.A. in Physics, Columbia University
B.A. in Mathematics, Columbia University
 

Michael Kim is an assistant professor of computer science. His research investigates foundational questions about responsible machine learning. Much of this work aims to identify problematic behaviors that emerge in machine-learned models and to develop algorithmic tools that provably mitigate such behaviors. More broadly, he is interested in how the theory of computation can provide insight into emerging societal and scientific challenges.

Rene Kizilcec is an associate professor of information science at Cornell, where he directs the Cornell Future of Learning Lab. Kizilcec studies behavioral, psychological, and computational aspects of technology in education to inform practices and policies that promote learning, equity, and academic and career success. His work has appeared in Science and the Proceedings of the National Academy of Sciences, won multiple awards, and is funded by the National Science Foundation, Schmidt Futures Foundation, and Jacobs Foundation. Kizilcec holds a Ph.D. in communication and M.Sc. in statistics from Stanford University.

Jon Kleinberg is a professor in both computer science and information science. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other online media. His work has been supported by an NSF Career Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Sloan Foundation Fellowship, and grants from Google, Yahoo!, and the NSF. He is a member of the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, and the American Philosophical Society.

Robert Kleinberg is an associate professor of computer science at Cornell University and a member of the field of information science. His research studies the design and analysis of algorithms and their applications to electronic commerce, networking, information retrieval, and other areas. Prior to receiving his doctorate from the Massachusetts Institute of Technology in 2005, Kleinberg spent three years at Akamai Technologies, where he assisted in designing the world's largest Internet Content Delivery Network. He is the recipient of a Microsoft Research New Faculty Fellowship, an Alfred P. Sloan Foundation Fellowship, and an NSF CAREER Award.
 
 

Allison Koenecke is an assistant professor of information science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. Her research on algorithmic fairness applies computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health.

Dan Kowal is an associate professor of statistics and data science. His research primarily revolves around three themes: (1) Bayesian models and algorithms for large and dependent (e.g., time series, spatial, functional) data, (2) modeling, generation, and imputation of mixed data, and (3) predictive inference for actionable and interpretable uncertainty quantification. He directs his research toward open questions in public health, epidemiology, physical activity data, economics, and finance. Recently, he has worked on addressing urgent issues related to racial inequities and biases in statistical modeling.

Dexter Kozen is the Joseph Newton Pew, Jr. Professor of Engineering Emeritus in the Department of Computer Science. His research interests span topics on the boundary of computer science and mathematics: design and analysis of algorithms, computational complexity theory, complexity of decision problems in logic and algebra, and logics and semantics of programming languages. He is the author of more than 200 research articles and four books. Kozen is a former Guggenheim fellow and a fellow of the Association of Computing Machinery, the American Association for the Advancement of Science, and the European Association of Theoretical Computer Science. He is a recipient of the John G. Kemeny Prize in Computing, an IBM Outstanding Innovation Award, the EATCS Award, the IEEE W. Wallace McDowell Award, and the ACM Alonzo Church Award.

Amy Kuceyeski is an adjunct associate professor in the Department of Statistics and Data Science and an associate professor of mathematics in the Radiology Department and Brain and Mind Research Institute at Weill Cornell Medicine. She performs research in quantitative neuroimaging of neurological disorders, including multiple sclerosis, stroke, traumatic brain injury and disorders of consciousness.
 
 

Volodymyr Kuleshov is the Joan Eliasoph, M.D. Assistant Professor of Computer Science at Cornell Tech, the Jacobs Technion-Cornell Institute, and the Cornell Ann S. Bowers College of Computing and Information Science. He obtained his Ph.D. in computer science from Stanford University, where he received the Arthur Samuel Best Thesis Award.

Owolabi Legunsen is an assistant professor of computer science. He is part of the Software Engineering Group and is broadly interested in research on software engineering, with a focus on software testing and runtime verification. Legunsen completed his Ph.D. in computer science at the University of Illinois at Urbana-Champaign, his master's degree in computer science at the University of Texas at Dallas, and his B.Sc. in computer engineering at Obafemi Awolowo University, Nigeria. He has received three ACM SIGSOFT distinguished paper awards (ASE 2016, ISSTA 2023, ISSTA 2024), an NSF CAREER Award, an Intel Rising Star Faculty Award, and a Microsoft Faculty Research Fellowship Finalist designation.

Sorin Lerner is the dean of the Cornell Ann S. Bowers College of Computing and Information Science, appointed Nov. 1, 2025. A professor of computer science, his research focuses on providing programmers with tools to make software more secure and reliable. 

Gilly Leshed is a Full Teaching Professor and the director of the MPS program in the Department of Information Science. Her teaching and research interests are in human-computer Interaction (HCI) and computer-supported cooperative work (CSCW), in which she uses quantitative and qualitative methods and technology design to examine how individuals and groups accomplish tasks and socialize, and the roles that information technology play in these interactions. She supervises the Cornell Digital Tech & Innovation Project Team and is a faculty house fellow at the William Keeton House.

Karen Levy is an associate professor of information science, an associate member of the faculty at Cornell Law School, and field faculty in sociology, science and technology studies, media studies, and data science.

Wei-Chiu Ma is an assistant professor of computer science at Cornell University. His research lies at the intersection of 3D/4D computer vision and robotics. He is interested in building AI systems that can understand, reconstruct, and re-simulate our dynamic world, and leveraging these capabilities to enable more robust autonomous systems or advanced entertainment applications.