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Research Expertise and Interest

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Name

Department

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

Yun S. Song

Dept of Statistics

Division of Computer Science (EECS)

computational biology, machine learning, applied probability and statistics

Peng Ding

Dept of Statistics

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

Elizabeth Purdom

Dept of Statistics

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

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

Nikita Zhivotovskiy

Dept of Statistics

mathematical statistics, applied probability, statistical learning theory

Avi Feller

Dept of Statistics

Goldman School of Public Policy

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

Adityanand Guntuboyina

Dept of Statistics

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

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

Ahmed Alaa

Computational Precision Health Program

machine learning and artificial intelligence, health care, statistics

Deborah Nolan

Dept of Statistics

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

Jennifer Listgarten

Division of Computer Science (EECS)

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

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

Uros Seljak

Dept of Physics

astrophysics, cosmology, machine learning, Bayesian statistics

Amanda Coston

Dept of Statistics

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

Ryan Giordano

Dept of Statistics

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

Bruno Olshausen

Dept of Neuroscience

School of Optometry

visual perception, computational neuroscience, computational vision

Nir Yosef

Division of Computer Science (EECS)

computational biology

Haiyan Huang

Dept of Statistics

applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification

Venkatesan Guruswami

Dept of Mathematics

Division of Computer Science (EECS)

theoretical computer science, coding theory, approximate optimization, randomness in computation, computational complexity

Samuel Lucas

Dept of Sociology

research methods, demography, sociology, social stratification, sociology of education, research statistics

Anil Aswani

Dept of Industrial Engineering & Operations Research

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

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