Statistics – MIDAS

Yixin Wang

October 4, 2021

Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference, with applications to recommender systems, text data, and genetics. She also works on algorithmic fairness and reinforcement learning, often via connections to causality. Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific ...

Ya’acov Ritov

May 1, 2020

My main interest is theoretical statistics as implied to complex model from semiparametric to ultra high dimensional regression analysis. In particular the negative aspects of Bayesian and causal analysis as implemented in modern statistics. An analysis of the position of SCOTUS judges.

Kean Ming Tan

March 9, 2020

I am an applied statistician working on statistical machine learning methods for analyzing complex biomedical data sets. I develop multivariate statistical methods such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction to uncover patterns from massive data set. Recently, I also work on topics related to robust statistics, non-convex optimization, and data ...

Yang Chen

January 10, 2018

Yang Chen received her Ph.D. (2017) in Statistics from Harvard University and then joined the University of Michigan as an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute of Data Science (MIDAS). She received her B.A. in Mathematics and Applied Mathematics from the University of Science and Technology of China. Research ...

Yuekai Sun

January 30, 2017

Yuekai Sun, PhD, is Assistant Professor in the department of Statistics at the University of Michigan, Ann Arbor. Dr. Sun’s research is motivated by the challenges of analyzing massive data sets in data-driven science and engineering. I focus on statistical methodology for high-dimensional problems; i.e. problems where the number of unknown parameters is comparable to or exceeds ...

Fred Feinberg

March 9, 2016

My research examines how people make choices in uncertain environments. The general focus is on using statistical models to explain complex decision patterns, particularly involving sequential choices among related items (e.g., brands in the same category) and dyads (e.g., people choosing one another in online dating), as well as a variety of applications to problems ...

Clayton Scott

March 9, 2016

I study patterns in large, complex data sets, and make quantitative predictions and inferences about those patterns. Problems I’ve worked on include classification, anomaly detection, active and semi-supervised learning, transfer learning, and density estimation. I am primarily interested in developing new algorithms and proving performance guarantees for new and existing algorithms.

Mahesh Agarwal

March 9, 2016

Prof. Agarwal’s is primarily interested in number theory, in particular in p-adic L-functions, Bloch-Kato conjecture and automorphic forms. His secondary research interests are polynomials, geometry and math education, Machine Learning, and healthcare analytics.

Elizaveta Levina

March 9, 2016

Elizaveta (Liza) Levina and her group work on various questions arising in the statistical analysis of large and complex data, especially networks and graphs. Our current focus is on developing rigorous and computationally efficient statistical inference on realistic models for networks. Current directions include community detection problems in networks (overlapping communities, networks with additional information ...

Moulinath Banerjee

March 9, 2016

Moulinath Banerjee, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor. Moulinath Banerjee was born and raised in India where he completed both his Bachelors and Masters in Statistics at the Indian Statistical Institute, Kolkata. He obtained his Ph.D. from the Statistics department at University ...