Research Interests

Statistics Department faculty and their research interests are listed below. Links to individual faculty pages are available by referring to the Faculty Directory.

Cecile Ané, Professor: Statistical inference for evolutionary biology, computational biology

Richard Chappell, Professor: Biostatistics, epidemiology, missing data, allometry

Peter Chien, Professor: Big data analytics, uncertainty quantification, A/B testing, design of experiments

Nicolas Gracia Trillos, Assistant Professor: Applied analysis, applied probability, computational probability and statistics, machine learning

Hyunseung Kang, Assistant Professor: 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, Professor: Biostatistics, statistical genomics & computational biology, censored data analysis

Bret Larget, Professor: Statistical applications in the biological sciences, Bayesian statistics, computational biology, phylogenetics.

Po-Ling Loh, Associate Professor: High-dimensional statistics, compressed sensing, nonconvex optimization, robust statistics, network inference

Wei-Yin Loh, Professor: Statistical inference; bootstrap theory and methods; decision tree algorithms for data mining and prediction with applications to missing value imputation, sample surveys, causal inference, and subgroup identification for precision medicine

Michael Newton, Professor: Stochastic modeling, computational biology, empirical Bayesian analysis, ranking, biomedical applications

Vivak Patel, Assistant Professor: Incremental Estimation and Asymptotic Statistics; Numerical Optimization Theory and Algorithms; Statistical Filtering; Applications to Dynamical Systems.

Sebastian Raschka, Assistant Professor: 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, Associate Professor: Optimization theory, information theory and theoretical statistics to study computational and statistical aspects of large-scale inference problems

Karl Rohe, Associate Professor: Regression and network clustering, machine learning, knowledge creation with statistics

Jun Shao, Professor: Inference, asymptotic theory, resampling methods, linear and nonlinear models, model selection, sample survey

Miaoyan Wang, Assistant Professor: Statistical machine learning, higher-order tensors, numerical multi-linear algebra, statistical/population genetics.

Yazhen Wang, Professor: 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

Brian Yandell, Professor: Nonparametrics, biometry, gene mapping, generalized linear models

Anru Zhang, Assistant Professor: High-dimensional statistical inference, statistical learning theory, tensor data analysis, compressed sensing and matrix recovery, applications in genomics

Chunming Zhang, Professor: 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 (Henry) Zhang, Professor: 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, Professor: Spatial statistics, spatio-temporal statistics, Markov random fields, agricultural statistics, environmental statistics, statistical ecology, environmental/population health, disease mapping, medical imaging, spatial demography

Affiliated Faculty

David Anderson, Professor: 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

Karl Broman, Professor: Statistical genomics, computational biology, statistical computing, data visualization, general applied statistics

Guanhua Chen, Assistant Professor: causal inference, semiparametric inference, statistical/machine learning and -omics data

Moo Chung, Associate Professor: Brain Image Analysis, Brain Network Analysis, Topological Data Analysis, Functional Data Analysis, Shape Analysis

Christina Kendziorski Newton, Professor: Statistical genetics and computational biology, Bayes and empirical Bayes methods

Ronald Gangnon, Professor: Spatial statistics, Bayes and empirical Bayes methods, ranking, age-period-cohort models, measurement error, epidemiology

Kyung Mann Kim, Professor: Sequential methods, clustered data analysis, categorical data analysis, biostatistics, clinical trials methods, epidemiology methods

Qiongshi Lu, Assistant Professor: Statistical genetics, genetic risk prediction, genome-wide association study, genome annotation, genomic data integration

Lu Mao, Assistant Professor: Survival analysis, semiparametric inference, design and analysis of clinical trials, nonparametric estimation under shape constraint

Robert Nowak, Professor: Machine learning and high-dimensional statistics

Mari Palta, Professor: Biostatistical methods and epidemiology

Timo Seppalainen, Professor: Probability theory, random environments and random potentials, interacting particle systems, and large deviation theory

Zhengzheng Tang, Assistant Professor: Statistical Genetics, Genetics Association Analysis, Meta-analysis, Microbiome & Metagenomic Data, Multi-Omics Data.

Menggang Yu, Professor: Clinical Biostatistics and Personalized Medicine, Causal Inference, Risk Prediction, Survival Analysis

Emeriti

Douglas Bates, Professor Emeritus: Nonlinear regression, statistical computing

Murray Clayton, Professor Emeritus: Applications of statistics to the agricultural, biological, and environmental sciences; spatial statistics, foundations

Norman Draper, Professor Emeritus: Experimental design, linear models, nonlinear estimation

Erik Nordheim, Professor Emeritus: Biological statistics, design and analysis, applied linear models

Richard Johnson, Professor Emeritus: Life testing & reliability, statistical inference, large sample theory, applied multivariate analysis

Robert Wardrop, Professor Emeritus: Online statistical education, statistics in sports

Kjell Doksum, Senior Research Scientist: Nonparametric regression, biostatistics

Kam-Wah Tsui, Professor Emeritus: Decision theory, survey sampling, statistical inference

Grace Wahba, Professor Emerita: Statistical machine learning, including complex models with heterogenous interacting inputs and outputs. Applications in Biostatistics and Physical Sciences.

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