**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.

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### Faculty Research Areas

### Jay Breidt

- Time series and spatio-temporal modeling
- Theory and methods of survey statistics
- Nonparametric regression
- Uncertainty quantification

- Environmental resource inventories
- Greenhouse gas inventory and the carbon cycle
- Ecological process models
- Biochemistry
- Geological sciences

### 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

### Don Estep

- Uncertainty quantification
- Error estimation
- Stochastic sensitivity analysis
- Stochastic inverse problems
- Adaptive computational methods
- Inverse problems for geometry
- Multiscale modeling

- Hurricane forecasting
- LIGO data for black holes
- Material science
- Flow in porous media
- Energy applications
- Networks
- Electromagnetic scattering

### Bailey Fosdick

- Statistical analysis of networks
- Bayesian methodology
- Methods for survey analysis
- Multivariate analysis

- Social sciences
- Population on ecology
- Science of team science
- STEM education

### Ann Hess

- Applied linear models
- Bioinformatics and statistical collaboration

- Applied statistics and biostatistics across a broad range

### Jennifer Hoeting

- Bayesian statistics
- Spatial statistics
- Computational statistics
- Model selection and uncertainty

- Statistical ecology
- Human and wildlife diseases
- Spatial ecology
- Climate science

### 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

### Mevin Hooten

- Bayesian methodology and computing
- Spatial and spatio-temporal statistics
- Point process modeling
- Hierarchical modeling using mathematical and statistical dynamical processes

- Ecological and environmental animal movement modeling
- Disease ecology
- Invasive species
- Spatial ecology
- Species distribution models

### Josh 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

### 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

### Julia Sharp

- Statistical consulting
- Statistical applications
- Experimental design and analysis
- Mixed effects models

- Public health
- Nutrition
- Food science
- Agricultural studies
- Life sciences
- Proteomics

### 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

### Zach Weller

- Bayesian statistics
- Spatial statistics
- Non-parametric statistics

- Applications in biology, ecology, energy, environmental issues and wildlife management

### Ander Wilson

- Bayesian statistics
- Confounder selection and model uncertainty
- Functional regression
- Environmental statistics

- Public health
- Environmental epidemiology
- Air pollution epidemiology
- Children’s health

### 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

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#### DEPARTMENT NEWS

### NYC utility improves safety of natural gas pipelines, thanks to CSU science

Research that uses Google Street View cars to map natural gas methane leaks is helping the nation’s largest metropolitan area prioritize tens of millions of dollars in infrastructure upgrades.

### GRANT AWARD: Next-generation biofuels with modified algae

Graham Peers and Wen Zhou will investigate next-generation biofuels and bioproducts using photosynthetic microalgae called diatoms.

### Tracking drives innovation in animal movement analysis

Mevin Hooten was guest editor of a special scientific journal issue on animal movement modeling.