Michael Jordan Dept of Statistics Division of Computer Science (EECS) computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
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
Yun S. Song Dept of Statistics Division of Computer Science (EECS) computational biology, machine learning, applied probability and statistics
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
Elizabeth Purdom Dept of Statistics computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics
Ryan Tibshirani Dept of Statistics statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning
Nikita Zhivotovskiy Dept of Statistics mathematical statistics, applied probability, statistical learning theory
Jennifer Listgarten Division of Computer Science (EECS) artificial intelligence, machine learning, computational biology, protein engineering, statistical design of experiments, statistical machine learning, experimental design
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
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
Kannan Ramchandran Division of Computer Science (EECS) Division of Electrical Engineering (EECS) statistical and sparse signal processing, adversarial and distributed machine learning, coded computing, privacy and security, scalable distributed video-on-demand delivery, coding theory, communications, information theory, peer-to-peer networking, blockchains
Deborah Nolan Dept of Statistics statistics, empirical process, high-dimensional modeling, technology in education
Peter Bartlett Dept of Statistics Division of Computer Science (EECS) machine learning, statistical learning theory, adaptive control
Michael DeWeese Dept of Neuroscience Dept of Physics machine learning, computation, systems neuroscience, auditory cortex, neural coding, statistical mechanics
Amanda Coston Dept of Statistics causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness
Peter Bickel Dept of Statistics statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
Jitendra Malik Division of Computer Science (EECS) computer science, electrical engineering, vision science, computer vision, robotics
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics
Ahmed Alaa Computational Precision Health Program machine learning and artificial intelligence, health care, statistics
Michael Jordan
