Department Directory

Ziv Goldfeld is an assistant professor in the School of Electrical and Computer Engineering, and a graduate field member in Computer Science, Statistics and Data Science, and the Center of Applied Mathematics at Cornell University. Before joining Cornell, he was a postdoctoral research fellow in LIDS at MIT, hosted by Yury Polyanskiy. Ziv graduated with a B.Sc., M.Sc., and Ph.D. (all summa cum laude) in Electrical and Computer Engineering from Ben Gurion University, Israel. His graduate advisor was Haim Permuter.

Ziv’s research interests include optimal transport theory, statistical learning theory, information theory, and applied probability. He seeks to understand the theoretical foundations of modern inference and information processing systems by formulating and solving mathematical models. Honors include the NSF CAREER Award, the IBM University Award, and the Rothschild Postdoctoral Fellowship.
 

Yongmiao Hong is the Ernest S. Liu Professor of Economics and International Studies Emeritus.
 

Henry's research interest include statistics, image science, bioinformatics, data science, machine learning, artificial intelligence, biomedical studies, and industrial applications.
 

Professor Hwang is also a Professor Emeritus in the Department of Mathematics.
Please see Professor Hwang's website for more information.
 

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. 

Kallus's research revolves around data-driven decision making in operations, the interplay of optimization and statistics in decision making and inference, and the analytical capacities and challenges of unstructured, large-scale, and web-based data. His works span basic theory, effective methodology, and novel applications and has been recognized by awards.

Nathan hails from the town of Haifa, Israel. He holds a PhD in Operations Research from MIT as well as a BA in Pure Mathematics and a BS in Computer Science both from UC Berkeley. Previously, Nathan has been a Visiting Scholar at USC's Department of Data Sciences and Operations and a Postdoctoral Associate at MIT's Operations Research and Statistics group.
 

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.

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.

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.
 
 

Tom Loredo's research combines statistical data analysis with astrophysical theory to rigorously test astronomical models and theories, particularly in the areas of high energy astrophysics (supernovae, gamma-ray bursts, black holes, neutron stars) and cosmology. His work focuses on problems that can benefit from development of new statistical methodology, and largely adopts the Bayesian approach to statistics, an approach first developed by the French physicist Laplace but largely abandoned until recently. He also uses the conventional "frequentist" approach, and is particularly interested in problems where these two statistical approaches produce different results. Recently Tom's work has also addressed statistical issues arising in the study of extrasolar planets and analysis of the distribution of trans-Neptunian objects (including Kuiper belt objects).

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.

Francesca Molinari is a professor in the economics department. Her research focuses on econometric theory and applied econometrics. She got her PhD in Economics from Northwestern University and her BS in Economics from the Universita degli Studi di Torino.