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Research Expertise and Interest

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Name

Department

Peng Ding

Dept of Statistics

Statistical causal inference, missing data, Bayesian statistics, applied statistics

Nikita Zhivotovskiy

Dept of Statistics

mathematical statistics, applied probability, statistical learning theory

Adityanand Guntuboyina

Dept of Statistics

nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory

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

Avi Feller

Dept of Statistics

Goldman School of Public Policy

applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences

Vadim Gorin

Dept of Mathematics

Dept of Statistics

integrable probability, random matrices, asymptotic representation theory, 2d statistical mechanics, interacting particle systems, high-dimensional statistics

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

Will Fithian

Dept of Statistics

Theoretical and Applied Statistics

Song Mei

Dept of Statistics

data science, statistics, machine learning

Perry de Valpine

Dept of Environmental Science, Policy & Management

population ecology, mathematical modeling and statistics

Jason Lee

Dept of Statistics

Division of Computer Science (EECS)

machine learning theory, optimization, statistics, deep learning

Ahmed Alaa

Computational Precision Health Program

machine learning and artificial intelligence, health care, statistics

Uros Seljak

Dept of Physics

astrophysics, cosmology, machine learning, Bayesian statistics

Deborah Nolan

Dept of Statistics

statistics, empirical process, high-dimensional modeling, technology in education

Sandya Subramanian

Computational Precision Health Program

translational medicine, computational neuroscience, physiology, statistics, mathematical modeling

Anil Aswani

Dept of Industrial Engineering & Operations Research

machine learning and artificial intelligence, precision health, optimization, statistics

Ryan Giordano

Dept of Statistics

machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics

Amanda Coston

Dept of Statistics

causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness

Elizabeth Purdom

Dept of Statistics

computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics

Alan Hammond

Dept of Statistics

probability, mathematical probability, statistical mechanics

Shirshendu Ganguly

Dept of Statistics

probability theory, statistical mechanics

Peter Bickel

Dept of Statistics

statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology

Jennifer Listgarten

Division of Computer Science (EECS)

artificial intelligence, machine learning, computational biology, protein engineering, statistical design of experiments, statistical machine learning, experimental design

Yun S. Song

Dept of Statistics

Division of Computer Science (EECS)

artificial intelligence, machine learning, applied probability and statistics, computational biology, computational genomics, human genetics

Andrew Bray

Dept of Statistics

statistical computing, data privacy, data science

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