Peng Ding Dept of Statistics Statistical causal inference, missing data, Bayesian statistics, applied statistics
Sandrine Dudoit School of Public Health Dept of Statistics statistics, machine learning, data science, applied statistics, statistical computing, computational biology, computational genomics, Precision Medicine, precision health
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied 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
Michael Jordan Dept of Statistics Division of Computer Science (EECS) computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
Lexin Li School of Public Health neuroimaging data analysis, deep brain stimulation, brain-computer-interface, statistical machine learning, Deep Learning, reinforcement learning, networks data analysis, functional data analysis, tensor analysis, ordinary differential equations, dimension reduction, high dimensional inference
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
Mark Wilson School of Education measurement, psychometrics, assessment, development of assessment resources, assessment systems
Peng Ding
