Major Research Interests of Faculty

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
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
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
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
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
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
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.