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
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
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
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
Deborah Nolan Dept of Statistics statistics, empirical process, high-dimensional modeling, technology in education
Elizabeth Purdom Dept of Statistics computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics
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
Dana R. Carney Haas School of Business nonverbal behavior, prejudice and discrimination, power and status, social perception, automaticity, social behavior and market outcomes
Yun S. Song Dept of Statistics Division of Computer Science (EECS) computational biology, machine learning, applied probability and statistics
Peter Bickel Dept of Statistics statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
Peter Bartlett Dept of Statistics Division of Computer Science (EECS) machine learning, statistical learning theory, adaptive control
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
Ryan Tibshirani Dept of Statistics statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning
Cameron Anderson Haas School of Business status hierarchies, psychology of power, self and interpersonal perception, team dynamics, influence processes, personality, emotions, groups and teams
Perry de Valpine Dept of Environmental Science, Policy & Management population ecology, mathematical modeling and statistics
Rasmus Nielsen Dept of Integrative Biology Dept of Statistics evolution, molecular evolution, population genetics, human variation, human genetics, phylogenetics, applied statistics, genetics, evolutionary processes, evolutionary biology
James Pitman Dept of Statistics fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence
Haiyan Huang
Haiyan Huang
Haiyan Huang
