Advising

Gongjun Xu is a professor in the Department of Statistics. He earned his Ph.D. in Statistics from Columbia University in 2013. His research focuses on latent variable models and statistical learning, with an emphasis on identification, interpretable representations, and reliable inference. He develops theory and methods for uncovering structure and quantifying uncertainty in complex data. He is the Co-Editor-in-Chief of the Journal of Educational and Behavioral Statistics. 

Kean Ming Tan is an associate professor in the Department of Statistics. He completed his Ph.D. in Biostatistics at the University of Washington in 2015. A central goal of his research is to develop flexible and robust methods with practical algorithms that can be reliably applied to large scale data sets. 

Saptarshi Chakraborty is an assistant professor in the Department of Statistics. He received his Ph.D. in Statistics from the University of California, Berkeley. Saptarshi's research focuses on the theoretical and methodological foundations of machine learning, with particular emphasis on deep learning theory, unsupervised learning, dimensionality reduction, optimal transport, and optimization. Saptarshi is also the recipient of the Eric L. Lehmann citation (2025) and Two-sigma PhD fellowship (2023-24).

Colin Fogarty is an associate professor in the Department of Statistics. He received his Ph.D. in Statistics from the Wharton School at the University of Pennsylvania in 2016. Much of his work pertains to the design and analysis of observational studies while assessing the robustness of a study's findings to unobserved biases. He is also interested in experimental design.

Johann Gagnon-Bartsch is an associate professor in the Department of Statistics. He earned his Ph.D. in Statistics from the University of California, Berkeley in 2012. His research focuses on causal inference, machine learning, and nonparametric methods with applications in the biological and social sciences.

Richard Guo is an assistant professor in the Department of Statistics. He completed his Ph.D. in Statistics at the University of Washington in 2021 and was a research associate at the University of Cambridge. His research topics include statistical foundations of causal inference, graphical models and ways to improve the replicability of data analysis. He is also a fan of British rock music. 

Yufeng Liu is the John D. MacArthur Professor in the Department of Statistics. He received his Ph.D. in Statistics from Ohio State University in 2004. His research interests include statistical machine learning, complex data analysis, precision medicine, bioinformatics, and e-commerce. He is an elected Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and an Elected Member of the International Statistical Institute (ISI).

Long Nguyen is a professor in the Department of Statistics. He was born and grew up in Vietnam, received his undergraduate education in Korea, and earned his Ph.D. degree from UC Berkeley, USA. His recent graduate level courses taught in Michigan include probability and distribution theory (STATS 510), theoretical statistics (STATS 610/611), Bayesian modeling (STATS 551), and Monte Carlo methods in statistics and computation (STATS 608). His research interest includes nonparametric Bayesian statistics, optimal transport for statistical inference, and machine learning with spatiotemporal data.

Yixin Wang is an assistant professor in the Department of Statistics. She completed her Ph.D. in Statistics at Columbia University in 2020. Her research interests include probabilistic generative modeling, Bayesian statistics, causal machine learning, causal inference, and applications. 

Yuekai Sun is an associate professor of statistics at the University of Michigan. Before coming to Michigan, Yuekai obtained his Ph.D. in Computational Mathematics from Stanford University, where he worked with Michael Saunders and Jonathan Taylor, and his BA (also in computational math) from Rice University. His research leverages statistical science to make AI more safe and reliable in the real world. Some topics of recent interest include AI alignment & evaluation, algorithmic fairness, and transfer learning.

Walter Dempsey is an assistant professor of biostatistics and assistant research professor at the Institute for Social Research. He received his Ph.D. in Statistics at the University of Chicago under the supervision of Peter McCullagh. His research focuses on statistical methods for digital and mobile health. Prior to joining U-M, he was a postdoctoral fellow in the Department of Statistics at Harvard University where he worked within the Statistical Reinforcement Learning Lab under the supervision of Susan Murphy.

Paramveer Dhillon is an associate professor in the School of Information at the University of Michigan. Paramveer received his Ph.D. (2015) in Computer Science from the University of Pennsylvania. His current research focuses on how Large Language Models (LLMs) reshape human creativity, decision-making, and information consumption. 

Xu Shi is an associate professor of biostatistics in the School of Public Health. Dr. Shi obtained her Ph.D. in Biostatistics from the University of Washington in 2017. Her research interests are electronic health records, causal inference, and AI monitoring. Dr. Shi co-leads the Causal Inference Core of the FDA's Sentinel Initiative Innovation Center to develop statistical methods to monitor the safety of FDA-regulated medical products and explore novel ways to utilize information from distributed EHR data partners.

Elle O'Brien is a lecturer and researcher at the University of Michigan School of Information. She studies how scientists program with generative AI programming tools. She also serves as the director of the Graduate Data Science Certificate program at the Michigan Institute for Data, AI and Society. Her research is generously supported by the Alfred P. Sloan Foundation's Digital Technology Fund and Schmidt Sciences.

Michał Dereziński is an assistant professor of computer science and engineering. He earned his Ph.D. in Computer Science at the University of California, Santa Cruz in 2018. Michał's research interests include machine learning and statistical learning theory, scalable stochastic and distributed optimization, randomized numerical linear algebra, and dimensionality reduction.

Alexander Rodríguez is an assistant professor of computer science and engineering. He obtained his Ph.D. from the Georgia Institute of Technology. Alexander's group's reasearch addresses problems at the intersection of machine learning, time series analysis, scientific modeling (AI for science), uncertainty quantification, and multi-agent systems, with primary applications in health sciences and engineering.

Moulinath Banerjee was born and raised in India where he completed both his bachelor's and master's degrees in statistics at the Indian Statistical Institute, Kolkata. He obtained his Ph.D. from the Department of Statistics at University of Washington, Seattle. Mouli's research interests are in the fields of non-standard problems, empirical process theory, threshold and boundary estimation, and graphical networks. He has a broad range of interests outside of statistics including classical music, literature, history, philosophy, physics and ancestral genetics. He is, also, most emphatically, a gourmet and believes that a life without good food is a life less lived.

Jonathan Terhorst joined the University of Michigan as an assistant professor of statistics after receiving his Ph.D. (2017) in Statistics from UC Berkeley. He is broadly interested in applications of statistics and machine learning to problems in biology, with a particular emphasis on statistical and population genetics.

Stilian Stoev joined the Statistics Department in 2005, after receiving a Ph.D. in Mathematics and Statistics from Boston University. His general research interests include applied probability, stochastic processes, and statistics of extremes with applications to engineering and physical sciences. Stilian was raised in Bulgaria, where he obtained his bachelor’s and master’s degrees in mathematics from Sofia University.

Gongjun Xu is a professor in the Department of Statistics. He earned his Ph.D. in Statistics from Columbia University in 2013. His research focuses on latent variable models and statistical learning, with an emphasis on identification, interpretable representations, and reliable inference. He develops theory and methods for uncovering structure and quantifying uncertainty in complex data. He is the Co-Editor-in-Chief of the Journal of Educational and Behavioral Statistics. 

Colin Fogarty is an associate professor in the Department of Statistics. He received his Ph.D. in Statistics from the Wharton School at the University of Pennsylvania in 2016. Much of his work pertains to the design and analysis of observational studies while assessing the robustness of a study's findings to unobserved biases. He is also interested in experimental design.