Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Peng Ding Dept of Statistics Statistical causal inference, missing data, Bayesian statistics, applied statistics
Sandrine Dudoit Dept of Statistics School of Public Health statistics, machine learning, data science, applied statistics, statistical computing, computational biology, computational genomics, Precision Medicine, precision health
Michael Jordan Dept of Statistics Division of Computer Science (EECS) computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
Nikita Zhivotovskiy Dept of Statistics mathematical statistics, applied probability, statistical learning theory
Samuel Lucas Dept of Sociology research methods, demography, sociology, social stratification, sociology of education, research statistics
Andrew Barshay Dept of History social thought, social sciences in modern Japan, marxism, Japanese history, Japanese-Russian relations
Adityanand Guntuboyina Dept of Statistics nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory
Henry Brady Dept of Political Science Goldman School of Public Policy comparative politics, public policy, electoral politics, political participation, survey research, program evaluation, statistical methods in the social sciences, social welfare policy, Soviet Union, inequality in America
Perry de Valpine Dept of Environmental Science, Policy & Management population ecology, mathematical modeling and statistics
Yun S. Song Dept of Statistics Division of Computer Science (EECS) computational biology, machine learning, applied probability and statistics
Christian Borgs Division of Computer Science (EECS) theoretical computer science, probability theory, combinatorics, complex networks, statistical physics, artificial intelligence, applications of artificial intelligence and machine learning in engineering and sciences
Deborah Nolan Dept of Statistics statistics, empirical process, high-dimensional modeling, technology in education
William (Bill) D. Thompson Dept of Psychology cognition, cognitive science, artificial intelligence, learning, Problem solving, Reasoning, decision-making, Bayesian statistics, natural language processing, machine learning, computational modeling, collective decision-making, social networks
Alistair Sinclair Dept of Statistics Division of Electrical Engineering (EECS) algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization
Elizabeth Purdom Dept of Statistics computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics
Ahmed Alaa Computational Precision Health Program machine learning and artificial intelligence, health care, statistics
Aaron Fisher Dept of Psychology Idiographic Science, Group-to-Individual Generalizability, Personalization, EMA, Time Series, physiology, Methods and Statistics
Amanda Coston Dept of Statistics causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness
Neil Gilbert School of Social Welfare social welfare, comparative welfare state analysis, child welfare, evaluation research, family policy, social security
Kurt C. Organista School of Social Welfare social welfare, race/ethnicity, HIV prevention, social behavior
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics
Avi Feller
Avi Feller
Avi Feller
