Department Directory

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:

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. 

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.

Thorsten Joachims is the Jacob Gould Schurman Professor of computer science and information science at Cornell University. He has also served as the interim dean for Cornell Bowers and as associate dean for research and as chair of the Department of Information Science. He is the director of the Cornell AI Initiative and Radical Collaboration. He has served as program chair of the ICML, KDD, and RecSys conferences, and he is a member of the IMLS Board and the SIGKDD Executive Committee. Thorsten Joachims joined Cornell in 2001 after finishing his Ph. D. as a student of Prof. Morik at the University of Dortmund, from where he also received a Diplom in computer science in 1997. 

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.

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.

David S. Matteson is a professor of statistics and data science at Cornell University and director of the National Institute of Statistical Sciences. He is also director of graduate studies in data science, and founding executive committee member for the Cornell Center for Data Science for Enterprise and Society. His research centers on statistical methods for modeling complex human and natural processes and systems, with expertise in dynamic, spatial, functional, and network data science and machine learning. He received a CAREER Award from the National Science Foundation, the Chancellor’s Award for Scholarship and Creative Activities from the State University of New York, the inaugural Ann S. Bowers Research Excellence Award, Faculty Research Awards from the Xerox/PARC Foundation and LinkedIn, and is a Fellow of the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He has served as lead PI and director for several National Science Foundation-funded collaborative institutes, and he founded and serves as editor-in-chief for the new open access ASA journal Data Science in Science.

Yang Ning is an associate professor of statistics and data science. The theme of his research is to develop statistical methods and theory to quantify the uncertainty (confidence interval and hypothesis test) in modern data sets, which are characterized by high dimensionality, complexity, and heterogeneity. He enjoys working at the interface of mathematical statistics, machine learning, and stochastic optimization. He is also interested in applied projects in genomics, neuroscience, epidemiology, and clinical trials. 

David Ruppert is the Andrew Schultz Jr. Professor of Engineering in the School of Operations Research and Information Engineering, and professor of statistics and data science at Cornell University. He received a B.A. in mathematics from Cornell in 1970, an M.A. in mathematics from the University of Vermont in 1973, and a Ph.D. in statistics and probability from Michigan State University in 1977. He was assistant and then associate professor of statistics at the University of North Carolina, Chapel Hill, from 1977 to 1987. He is a Fellow of the ASA and IMS, and received the Wilcoxon Prize in 1986. Ruppert was named a "highly cited" researcher by ISIHighlyCited.com and was ranked 21st in mathematics by journal citations. 

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.

Y. Samuel (Sam) Wang is an assistant professor of statistics and data science. Prior to joining Cornell in 2021, he was a post-doc at the University of Chicago’s Booth School of Business, and he completed his Ph.D. in statistics at the University of Washington. He also previously worked as a management consultant before his Ph.D. studies. His primary research areas are graphical models and causal discovery.