Adityanand Guntuboyina Dept of Statistics nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information 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
Ying Cui Dept of Industrial Engineering & Operations Research continuous optimization, optimization under uncertainty, statistical estimations, machine learning, artificial intelligence
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
Ryan Tibshirani Dept of Statistics statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning
Michael Jordan Dept of Statistics Division of Computer Science (EECS) computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Peter Bickel Dept of Statistics statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
Elizabeth Purdom Dept of Statistics computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics
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
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics
Deborah Nolan Dept of Statistics statistics, empirical process, high-dimensional modeling, technology in education
Yun S. Song Dept of Statistics Division of Computer Science (EECS) computational biology, machine learning, applied probability and statistics
Amanda Coston Dept of Statistics causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness
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
Kameshwar Poolla Dept of Mechanical Engineering Division of Electrical Engineering (EECS) modeling, control, estimation, smart grids, renewable energy, electricity markets, transportation networks, integrated circuit design and manufacturing
Sophia Rabe-Hesketh School of Education biostatistics, educational statistics, latent variable models, missing data methods, multilevel models, generalized linear latent and mixed models, hierarchical models, longitudinal data, Item response models, structural equation models
Peter Bartlett Dept of Statistics Division of Computer Science (EECS) machine learning, statistical learning theory, adaptive control
Samuel Lucas Dept of Sociology research methods, demography, sociology, social stratification, sociology of education, research statistics
Adityanand Guntuboyina
Adityanand Guntuboyina
Adityanand Guntuboyina
