Department Directory

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

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 is also the Vice Provost for Artificial Intelligence Strategy at Cornell, where he leads the Cornell AI Initiative and the AI Radical Collaboration. He previously served as the interim dean for Cornell Bowers, as associate dean for research for Cornell Bowers, and as chair of the Department of Information Science. He has also 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.
 

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.

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. 
 

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. 

Michael Nussbaum is a professor of mathematics. His research program focuses on asymptotic methods in quantum statistics, in particular on hypothesis testing and discrimination between states of quantum systems. Further topics are the equivalence theory of statistical experiments, asymptotic inference in locally stationary time series and adaptive nonparametric hypothesis testing.