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
- Extreme value analysis
- Tail dependence
- Risk of rare events
- Heavy tails
- Modeling
- Atmospheric science
- Climate modeling
- Energy-related environmental research
Kirsten Eilertson
- Applied statistics
- Generalized linear mixed models
- Latent variable and state space models
- Behavioral neuroscience
- Biomechanics
- Functional genomics
- Quantitative epidemiology
- Generally public heath and medicine
Ann Hess
- Applied linear models
- Bioinformatics and statistical collaboration
- Applied statistics and biostatistics across a broad range
Andee Kaplan
- 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
- Social sciences
- Data with network structures
- Multiple source data
Kayleigh Keller
- Environmental biostatistics
- Spatiotemporal modeling
- Measurement error
- Spatial confounding
- Hierarchical models
- Public health
- Air pollution epidemiology
- Environmental engineering
- Infectious disease
Piotr Kokoszka
- Models for dependent data
- Asymptotic theory
- Functional data analysis
- Time series
- Spatio-temporal statistics
- Change point analysis
- Extreme value theory and heavy tails
- Finance
- Climate science
- Physical networks
- Space physics
Matt Koslovsky
- Bayesian methodology
- Nonparametric Bayes
- Variable selection
- Statistical computing
- Markov models
- mHealth
- Public health
- Cancer Prevention
- Microbiome
- Nutrition
Mary Meyer
- Nonparametric function estimation with constraints involving shapes and orderings with likelihood-based inference methods
- Generalized additive models
- Robust regression
- Discrete choice models in economics
- Shape selection in forestry models
Ben Shaby
- Spatial statistics
- Bayesian modeling
- Bayesian computation
- Extreme values
- Climate and weather
- Geophysics
- Neurodegenerative diseases like Alzheimer’s and ALS
- High-throughput biological data
Haonan Wang
- 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
- Neuroscience
- Communication networks
- Sensor data
Ander Wilson
- Bayesian statistics
- Confounder selection and model uncertainty
- Functional regression
- Environmental statistics
- Public health
- Environmental epidemiology
- Air pollution epidemiology
- Children’s health
Tianjian Zhou
- Bayesian methodology and computing
- Bayesian hierarchical modeling
- Bayesian nonparametrics
- Bayesian hypothesis testing
- Methodology for missing data
- Clinical trials
- Genomics
- Infectious diseases
Wen Zhou
- High dimensional inference
- Statistical machine learning
- Graphical modeling
- Statistical genomics and genetics
- Bioinformatics
- Inverse problems
- Multivariate time series
- Genetics and genomics
- Proteomics and structure biology
- Omics analysis
- Integrative analysis
- System biology
- Econometrics and finance
DEPARTMENT NEWS
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.
