Department Directory

John Abowd is the Edmund Ezra Day Professor Emeritus in Economics, where he also serves as the Director of Graduate Studies, and Statistics and Data Science. Professor Abowd is a member in the graduate fields of Economics, Information Science, Industrial and Labor Relations and Statistics. He is a professor in the department of Information Science as well. He is the Director of the Labor Dynamics Institute at Cornell, an institution whose purpose is to to create and make accessible novel data on the dynamics of the labor markets. 
 

Sumanta Basu is an associate professor of statistics and data science. He is broadly interested in developing statistical machine learning methods for structure learning and prediction of complex, high-dimensional systems arising in biological and social sciences. He is currently working in two areas: (1) network modeling of high-dimensional time series; and (2) detecting high-order interactions in complex biological systems using randomized tree ensembles. He also works closely with scientists and economists on a wide range of problems including prostate cancer progression, large scale metabolomics, and systemic risk monitoring in financial markets.

James Booth is the department chair and professor in the Department of Statistics and Data Science. His research interests involve basic statistical methodology, including the bootstrap and Monte Carlo methods, clustering, exact inference, mixed models, generalized linear models, and also applications in bioinformatics. 

Jelena Bradic is a professor of statistics and data science. Bradic’s research centers on causal inference, machine learning, and high-dimensional robust statistics, with applications in areas including public health and policy learning. Before joining Cornell, she held positions at the University of California, San Diego, in both the Department of Mathematics and the Halıcıoğlu Data Science Institute. She received her Ph.D. in operations research and financial engineering, from Princeton University.

Florentina Bunea is a professor of statistics and data science and a member of the graduate fields of statistics, applied mathematics, and computer science. Her research is broadly centered on statistical machine learning theory and high-dimensional statistical inference. She is interested in developing new methodology accompanied by sharp theory for solving a variety of problems in data science and in the growing area of AI output evaluation. She continues to be interested in the general areas of mixture modeling, latent space estimation, sparsity and dimension reduction in high dimensions, and statistical optimal transport, as well as their applications, most recently to large language models and immunology, among others. Specific research foci include:

Prof. Yun-chien Chang is Jack G. Clarke Professor in East Asian Law at Cornell Law School and also directs the Clarke Program in East Asian Law & Culture. His current academic interests focus on economic, empirical and comparative analysis of private law (particularly property law), as well as empirical studies of the judicial system. 
 

Jim Dai is a professor in the School of Operations Research and Information Engineering (ORIE) of Cornell University. He is currently on leave from the Chandler Family Chair of Industrial & Systems Engineering at Georgia Institute of Technology, where he has been a faculty member for 22 years. He is a Special Term Professor at Tsinghua University and a Visiting Professor in Decision Sciences at National University of Singapore. For more than twenty years, he has worked on stochastic models arising from communications, manufacturing, and service systems that include data switches, semiconductor wafer fabrication lines, call centers, and healthcare-delivery systems.

Sreyoshi Das designs and offers courses on applications of statistics and data science in the industry, with specific emphasis in the areas of economics and finance. Her courses aim to integrate academic training with hands-on work experience. Before joining Cornell in 2022, she worked in economic consulting where she developed a variety of quantitative and qualitative analyses to support testifying experts, client attorneys, government agencies, and corporations. Das received her Ph.D. in economics from the University of Michigan in 2017, where she conducted research on banking and systemic risk, financial markets in emerging economies, and behavioral macroeconomics.

Christopher De Sa is an associate professor of computer science and a member of the Cornell Machine Learning Group where he leads the Relax ML Lab. His research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous and low-precision stochastic gradient descent (SGD) and Markov chain Monte Carlo. His work builds towards using these techniques to construct data analytics and machine learning frameworks, including for deep learning, that are efficient, parallel, and distributed. De Sa received his B.S., M.A., and Ph.D. from Stanford University in electrical engineering.

Tom DiCiccio is an associate professor of social statistics in the ILR School.

Ahmed El Alaoui is an assistant professor of statistics and data science. His research interests revolve around high-dimensional phenomena in statistics and probability theory, statistical physics, algorithms, and problems where these areas meet. Prior to joining the Cornell faculty in 2021, he was a postdoctoral researcher at Stanford University, hosted by Andrea Montanari. He received his Ph.D. in electrical engineering and computer sciences from UC Berkeley in 2018, advised by Michael I. Jordan. 

Peter Frazier received a B.S. in Physics and Engineering/Applied Science from the California Institute of Technology in 2000, after which he spent several years in industry as a software engineer, working for two different start-up companies and for the Teradata division of NCR. In 2005, he entered graduate school in the Department of Operations Research & Financial Engineering at Princeton University, and received an M.A. in 2007 and a Ph.D. in 2009. He joined the faculty at Cornell in 2009 as an Assistant Professor in the School of Operations Research & Information Engineering. His research is in sequential decision-making under uncertainty and optimal methods for collecting information, focusing on applications in simulation and health care. He is the recipient of a CAREER Award from the National Science Foundation and a Young Investigator Award from the Air Force Office of Scientific Research.
 

Sainyam Galhotra is an assistant professor in computer science. Before that, he was a Computing Innovation Fellow pursuing postdoctoral research at the University of Chicago. The goal of his research is to develop data science tools for effective and responsible analytics.

Kyra Gan is an Assistant Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. Her research interests include adaptive/online algorithm design in personalized treatment (including micro-randomized trials and N-of-1 trials) under constraint settings, computerized/automated inference methods (e.g., targeted learning with RKHS), robust causal discovery in medical data, and fairness in organ transplants. More broadly, she is interested in bridging the gap between research and practice in healthcare.
Prior to Cornell Tech, she was a postdoctoral fellow at the Statistical Reinforcement Lab at Harvard University. She received her Ph.D. in Operations Research in 2022 from Carnegie Mellon University at the Tepper School of Business. She received her B.A.s in Mathematics and Economics from Smith College in 2017. She is a recipient of the 2021 Pierskalla Best Paper Award and the 2021 CHOW Best Student Paper Award in the Category of Operations Research and Management Science.
Selected Publications

Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, and Susan A. Murphy. “Contextual Bandits with  Budgeted Information Reveal.” Preprint, 2023
 
Brian Cho*, Yaroslav Mukhin*, Kyra Gan, Ivana Malencia. “Kernel Debiased Plug-in Estimation.” Preprint, 2023
 
Kyra Gan*, Su Jia*, Andrew Li. “Greedy Approximation Algorithms for Active Sequential Hypothesis Testing." Advances in Neural Information Processing Systems, 2021
 
Kyra Gan, Andrew Li, Zachary Lipton, and Sridhar Tayur. “Causal Inference with Selectively Deconfounded Data.” International Conference on Artificial Intelligence and Statistics, 2021
 
Arisha Patel*, Kyra Gan*, Andrew Li, Jeremy Weiss, Seyed Mehdi Nouraie, Sridhar Tayur, and Enrico M Novelli. “Machine Learning Algorithms in Predicting Hospital Readmissions in Sickle Cell Disease.” British Journal of Haematology, 2020

Selected Awards and Honors

2021 INFORMS Pierskalla Best Paper Award
 
2021 CHOW Best Student Paper Award in the Category of Operations Research and Management Science
 
Tata Consultancy Services Fellowship, 2020

 

Christophe Giraud was a student of the École Normale Supérieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the École Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012.