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
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
Patrick Bradshaw School of Public Health epidemiologic methods, Bayesian methods, cancer epidemiology
Alan Hubbard School of Public Health causal inference, targeted learning, statistical issues in epidemiology, precision medicine and public health
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
Mark van der Laan Dept of Statistics School of Public Health targeted learning, real-world data integration in RCTs, sequential adaptive designs, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies
Sam Pimentel Dept of Statistics causal inference, health services & policy analysis, biostatistics, discrete optimization
Daniel Okamoto Dept of Integrative Biology population dynamics of exploited species, marine biology, community ecology, ecological modeling, biostatistics
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
Elizabeth Purdom
