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
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
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
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
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 Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, 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
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
Peter Bickel Dept of Statistics statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
Ryan Tibshirani Dept of Statistics statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning
Bethany L. Goldblum Dept of Nuclear Engineering applied nuclear physics, neutron detection, scintillation physics, machine learning applications, nuclear weapon policy
Adityanand Guntuboyina Dept of Statistics nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory
Manisha Shah Goldman School of Public Policy applied microeconomics, international development, global health, gender
Ilan Adler Dept of Industrial Engineering & Operations Research mathematical programming, computational game theory, applied probability
David Sunding Dept of Agricultural & Resource Economics environmental economics, industrial organization, applied econometrics, law and economics
Xin Guo Dept of Industrial Engineering & Operations Research stochastic control and games, theory of machine learning and applications to medical and financial data analysis, applied probability and stochastic models
Peng Ding
Peng Ding
Peng Ding
