Peng Ding Dept of Statistics Statistical causal inference, missing data, Bayesian statistics, applied statistics
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics
Joseph Lewnard School of Public Health infectious diseases, antimicrobial resistance, public health surveillance, mathematical modeling, Bayesian inference
Xueyin (Snow) Zhang Dept of Philosophy Bayesian epistemology, decision theory, philosophy of probability, Chinese philosophy
Patrick Bradshaw School of Public Health epidemiologic methods, Bayesian methods, cancer epidemiology, cardiometabolic risk
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Karthik Shekhar Dept of Chemical & Biomolecular Engineering cellular and systems biology, statistical inference, single-cell genomics
Sam Pimentel Dept of Statistics causal inference, health services & policy analysis, biostatistics, discrete optimization
Maya L. Petersen School of Public Health Computational Precision Health Program causal inference, precision health, targeted learning, HIV, global health, pandemics
Jingshen Wang School of Public Health Precision Medicine, causal inference, adaptive experiment, machine learning and artificial intelligence
Amanda Coston Dept of Statistics causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness
Alan Hubbard School of Public Health causal inference, targeted learning, statistical issues in epidemiology, precision medicine and public health
Stephanie Zonszein Dept of Political Science immigrant integration, intergroup relations, causal inference, race and ethnic politics, political behavior
Ryan Tibshirani Dept of Statistics statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning
Kirk Bansak Dept of Political Science causal inference, experimental design and analysis, algorithmic decision-making, refugee resettlement and asylum politics, public opinion, survey methodology
Thad Dunning Dept of Political Science political economy, ethnic politics, comparative clientelism in developing countries, research design, causal inference, statistical methods, multi-method research
Xiudi Li School of Public Health causal inference, data integration, federated learning and transfer learning, electronic health records, clinical trials, High Dimensional Data Analysis
Jimmy A. McGuire Dept of Integrative Biology historical biogeography, evolutionary biology, Southeast Asia, population genetics, hummingbirds, functional morphology, vertebrate systematics, phylogenetic analysis, life history evolution, Bayesian methods, Southeast Asian flying lizards
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
David Harding Dept of Sociology poverty, inequality, causal inference, mixed methods, incarceration, prisoner reentry, education, neighborhoods, urban, community, adolescence, public impact research/scholarship
William (Bill) D. Thompson Dept of Psychology cognition, cognitive science, artificial intelligence, learning, Problem solving, Reasoning, decision-making, Bayesian statistics, natural language processing, machine learning, computational modeling, collective decision-making, social networks
Philip Marcus Dept of Mechanical Engineering algorithms, fluid mechanics, nonlinear dynamics, atmospheric flows, convection, ocean flows, numerical analysis, wind energy, Bayesian optimization, neural networks, turbulence, planet formation, internal gravity waves, inertial waves, desalination, protoplanetary disks
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
Peng Ding
