Statistics Department faculty and their major research interests are listed below. For those faculty holding joint appointments, the programs (in addition to Statistics) in which they hold their joint appointment are also listed. Links to individual faculty pages are available by referring to the Faculty Directory.
- David Anderson
- Affiliate, Mathematics Department
- Developing and analyzing new computational methods for the stochastic models that arise in the biosciences; theoretical study of the mathematical models arising in the biosciences
- Cecile Ane
- Statistics / Botany Department
- Statistical inference for evolutionary biology, Computational Biology
- Doug Bates
- Emeritus
- Nonlinear Regression, Statistical Computing
- Karl Broman
- Affiliate, Biostatistics & Medical Informatics Department
- Statistical genomics, computational biology, statistical computing, data visualization, general applied statistics
- Rick Chappell
- Statistics / Biostatistics & Medical Informatics Department
- Biostatistics, epidemiology, missing data, allometry
- Peter Chien
- Big data analytics, uncertainty quantification, A/B testing, design of experiments
- Moo Chung
- Affiliate, Biostatistics & Medical Informatics Department
- Brain Image Analysis, Brain Network Analysis, Computational Topology, Functional Data Analysis, Partial Differential Equations
- Murray Clayton
- Emeritus
- Statistics / Plant Pathology (CALS)
- Applications of statistics to the agricultural, biological, and environmental sciences; spatial statistics, foundations
- Kjell Doksum
- Senior Research Scientist
- Nonparametric regression, biostatistics, high dimensional data analysis
- Norman Draper
- Emeritus
- Experimental Design, Linear Models, Nonlinear Estimation
- Ronald Gangnon
- Affiliate, Biostatistics & Medical Informatics Department
- Spatial statistics, Bayes and empirical Bayes methods, ranking, age-period-cohort models, measurement error, epidemiology
- Richard Johnson
- Emeritus
- Life Testing and Reliability, Statistical Inference, Large Sample Theory, Applied Multivariate Analysis
- Hyunseung Kang
- Causal inference, instrumental variables, and econometrics, developing methods for causal inference using large observational data with applications to epidemiology, genetics, social policy evaluation, and online data
- Sunduz Keles
- Statistics / Biostatistics & Medical Informatics Department
- Biostatistics, statistical genomics & computational biology, censored data analysis
- Christina Kendziorski Newton
- Affiliate, Biostatistics & Medical Informatics Department
- Statistical genetics and computational biology, Bayes and empirical Bayes methods
- Bret Larget
- Statistics / Botany Department
- Statistical applications in the biological sciences, Bayesian statistics, computational biology, phylogenetics
- Po-Ling Loh
- Affiliate, Electrical & Computer Engineering Department
- High-dimensional statistics, compressed sensing, nonconvex optimization, robust statistics, network inference
- Wei-Yin Loh
- Statistical inference; bootstrap theory and methods; decision tree algorithms for data mining and prediction with applications to missing value imputation, analysis of sample surveys, and subgroup identification for personalized medicine
- Qiongshi Lu
- Affiliate, Biostatistics & Medical Informatics Department
- Statistical genetics, genetic risk prediction, genome-wide association study, genome annotation, genomic data integration
- Lu Mao
- Affiliate, Biostatistics & Medical Informatics Department
- Survival analysis, semiparametric inference, design and analysis of clinical trials, nonparametric estimation under shape constraint
- Michael Newton
- Statistics / Biostatistics & Medical Informatics Department
- Stochastic modeling, computational biology, empirical Bayesian analysis, ranking
- Erik Nordheim
- Emeritus
- Biological Statistics, Design and Analysis, Applied Linear Models
- Robert Nowak
- Affiliate, Electrical & Computer Engineering Department
- Machine learning and high-dimensional statistics
- Mari Palta
- Affiliate, Departments of Population Health Sciences / Department of Biostatistics & Medical Informatics
- Biostatistical methods and epidemiology
- Vivak Patel
- Incremental Estimation and Asymptotic Statistics; Numerical Optimization Theory and Algorithms; Statistical Filtering; Applications to Dynamical Systems
- Sebastian Raschka
- Deep learning with a focus on privacy protection and protection against adversarial attacks, automatic machine learning (AutoML), machine learning model evaluation, and machine learning applied to molecular modeling
- Garvesh Raskutti
- Optimization Theory, Information theory and Theoretical statistics to study computational and statistical aspects of large-scale and inference problems
- Paul Rathouz
- Affiliate, Biostatistics & Medical Informatics Department
- Missing data in models for highly stratified or longitudinal data, generalized linear models, methods for behavior genetic designs, and outcome-dependent sampling for longitudinal data. Most of his current applied statistical work is in the areas of developmental psychopathology and health services research.
- Karl Rohe
- Regression and network clustering, machine learning, knowledge creation with statistics
- Timo Seppalainen
- Affiliate, Mathematics Department
- Motion in a random medium, interacting particle systems, large deviation theory
- Jun Shao
- Inference, asymptotic theory, resampling methods, linear and nonlinear models, model selection, sample survey
- Yajuan Si
- Affiliate, Department of Population Health Sciences
- Bayesian statistics, missing data analysis, complex survey inference and causal inference
- Nicolas Garcia Trillos
- Applied analysis, applied probability, computational probability and statistics, machine learning
- Kam Wah Tsui
- Decision Theory, Survey Sampling, Statistical Inference
- Grace Wahba
- Statistical machine learning, including complex models with heterogenous interacting inputs and outputs. Applications in Biostatistics and Physical Sciences
- Miaoyan Wang
- Statistical machine learning, higher-order tensors, numerical multi-linear algebra, statistical/population genetics
- Yazhen Wang
- Financial statistics & financial data science, quantum computing, quantum tomography, high-dimensional statistical inference, machine learning, wavelets, nonparametric smoothing, change points, long-memory process, and order restricted statistical inferences
- Robert Wardrop
- Emeritus
- Online statistical education, statistics in sports
- Brian Yandell
- Statistics / Horticulture (CALS)
- Nonparametrics, biometry, gene mapping, generalized linear models
- Menggang Yu
- Affiliate, Biostatistics & Medical Informatics Department
- Clinical Biostatistics and Personalized Medicine, Causal Inference, Risk Prediction, Survival Analysis
- Ming Yuan
- Statistics / WIMR (currently on leave)
- Stochastic modeling and inference, Nonparametric and high dimensional inference, Biomedical and financial applications
- Anru Zhang
- High-dimensional statistical inference, statistical learning theory, tensor data analysis, compressed sensing and matrix recovery, applications in genomics
- Chunming Zhang
- Neuroinformatics and bioinformatics, machine learning and data mining, multiple testing, large-scale simultaneous inference and application, statistical methods in finance econometrics, non- and semi-parametric estimation and inference, functional and longitudinal data analysis
- Zhengjun Zhang
- Extreme value analytics for big data and financial time series analysis; risk analysis in finance, insurance, environmental studies, and seismic data; nonlinear/asymmetric causal inference; hi-dimensional inference; medical statistics; stochastic optimization and simulation technique; Bayesian inference for time series
- Jun Zhu
- Statistics / Entomology (CALS), Biometry Program
- Spatial statistics, spatio-temporal statistics, environmental statistics, spatial demography, statistical ecology
See Department of Biostatistics & Medical Informatics for additional Biostatistics Faculty Research Interests.