Statistics is the science of inferring knowledge from data and describing uncertainty in those inferences. It plays a central role in scientific research, social policy, and governance. The Department of Statistics has a world-class record of success in education, research and service and has an out-sized impact on campus because of its engagement in educating students in all disciplines and interdisciplinary research. Learn more about our applied and interdisciplinary research impact as well as our long list of research collaborations and interactions here.

Faculty Research Areas

Dan Cooley

Statistics + Probability Research
  • Extreme value analysis
  • Tail dependence
  • Risk of rare events
  • Heavy tails
  • Modeling
Applied + Interdisciplinary Research
  • Atmospheric science
  • Climate modeling
  • Energy-related environmental research

Kirsten Eilertson

Statistics + Probability Research
  • Applied statistics
  • Generalized linear mixed models
  • Latent variable and state space models
Applied + Interdisciplinary Research
  • Behavioral neuroscience
  • Biomechanics
  • Functional genomics
  • Quantitative epidemiology
  • Generally public heath and medicine

Ann Hess

Statistics + Probability Research
  • Applied linear models
  • Bioinformatics and statistical collaboration
Applied + Interdisciplinary Research
  • Applied statistics and biostatistics across a broad range

Andee Kaplan

Statistics + Probability Research
  • Computationally scalable statistics methods
  • Record linkage (Entity resolution or de-duplication)
  • Markov chain monte carlo (MCMC)
  • Network analysis
  • Spatial re-sampling and generalized statistical machine learning methods
  • Interactive statistical graphics
  • Reproducible research
Applied + Interdisciplinary Research
  • Social sciences
  • Data with network structures
  • Multiple source data

Kayleigh Keller

Statistics + Probability Research
  • Environmental biostatistics
  • Spatiotemporal modeling
  • Measurement error
  • Spatial confounding
  • Hierarchical models
Applied + Interdisciplinary Research
  • Public health
  • Air pollution epidemiology
  • Environmental engineering
  • Infectious disease

Piotr Kokoszka

Statistics + Probability Research
  • Models for dependent data
  • Asymptotic theory
  • Functional data analysis
  • Time series
  • Spatio-temporal statistics
  • Change point analysis
  • Extreme value theory and heavy tails
Applied + Interdisciplinary Research
  • Finance
  • Climate science
  • Physical networks
  • Space physics

Matt Koslovsky

Statistics + Probability Research
  • Bayesian methodology
  • Nonparametric Bayes
  • Variable selection
  • Statistical computing
  • Markov models
Applied + Interdisciplinary Research
  • mHealth
  • Public health
  • Cancer Prevention
  • Microbiome
  • Nutrition

Mary Meyer

Statistics + Probability Research
  • Nonparametric function estimation with constraints involving shapes and orderings with likelihood-based inference methods
  • Generalized additive models
  • Robust regression
Applied + Interdisciplinary Research
  • Discrete choice models in economics
  • Shape selection in forestry models

Ben Shaby

Statistics + Probability Research
  • Spatial statistics
  • Bayesian modeling
  • Bayesian computation
  • Extreme values
Applied + Interdisciplinary Research
  • Climate and weather
  • Geophysics
  • Neurodegenerative diseases like Alzheimer’s and ALS
  • High-throughput biological data

Haonan Wang

Statistics + Probability Research
  • Object oriented data analysis
  • Functional data analysis
  • Functional dynamic modeling
  • Spatial and spatiotemporal modeling
  • Statistical learning for big data
  • Time series
  • Statistical modeling for complex networks
Applied + Interdisciplinary Research
  • Neuroscience
  • Communication networks
  • Sensor data

Ander Wilson

Statistics + Probability Research
  • Bayesian statistics
  • Confounder selection and model uncertainty
  • Functional regression
  • Environmental statistics
Applied + Interdisciplinary Research
  • Public health
  • Environmental epidemiology
  • Air pollution epidemiology
  • Children’s health

Tianjian Zhou

Statistics + Probability Research
  • Bayesian methodology and computing
  • Bayesian hierarchical modeling
  • Bayesian nonparametrics
  • Bayesian hypothesis testing
  • Methodology for missing data
Applied + Interdisciplinary Research
  • Clinical trials
  • Genomics
  • Infectious diseases

Wen Zhou

Statistics + Probability Research
  • High dimensional inference
  • Statistical machine learning
  • Graphical modeling
  • Statistical genomics and genetics
  • Bioinformatics
  • Inverse problems
  • Multivariate time series
Applied + Interdisciplinary Research
  • Genetics and genomics
  • Proteomics and structure biology
  • Omics analysis
  • Integrative analysis
  • System biology
  • Econometrics and finance


CSU study finds disparities in natural gas leak prevalence in U.S. urban areas

Over a several-year period, natural gas pipeline leaks were more prevalent in neighborhoods with low-income or majority non-white populations than those with high income or predominately white populations.

Assistant professor develops novel methods for analyzing microbiome data

Human microbiome research seeks to better understand the role of our microbial communities and how they interact with their host, respond to their environment and influence disease. 

Statistics professor honored for expertise in object-oriented data analysis

Haonan Wang’s creativity and research excellence recently earned him election as a Fellow of the American Statistical Association, the world’s largest community of statisticians.

By storing ‘sketches’ of data, computer scientists seek to transform urban systems

A research team is developing a system for streamlining and managing vast datasets that could advance research in urban sustainability.