Applied Statistics Master of Applied Statistics (M.A.S.)


Kirsten Eilertson – Associate Professor and Director of MAS
  • B.A., Math, St. Olaf College
  • Ph.D., Statistics, Cornell University

Kirsten Eilertson is an applied statistician and splits her time between collaborative research, consulting, and teaching. As an applied statistician, she has worked with scientists in a variety of fields from evolutionary biology, behavioral neuroscience, biomechanics, functional genomics, and most recently quantitative epidemiology. Her research involves the application of statistical modeling to address specific challenges. Eilertson is a member of the Vaccine Impact Modelling Consortium. Her work has contributed to the development of a novel state-space model for estimating measles disease burden at the country level. The primary aim of this work is to improve understanding of the impact on public health measures.

Jay Breidt – Professor
  • Ph.D., Statistics, Colorado State University
  • M.S., Statistics, Colorado State University
  • B.A., Mathematics and English Literature, The College of Idaho

Jay Breidt has expertise in survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models. He has an extensive record of refereed publications and has presented over 130 invited short courses, conference talks, and academic seminars. Since 1991, his research has been supported continuously by a variety of agencies including the National Science Foundation, National Institutes of Health, Department of Homeland Security, EPA, US Forest Service, and NASA. Breidt is the Reviews Editor for the Journal of the American Statistical Association and The American Statistician. He serves on the U.S. Bureau of the Census Scientific Advisory Committee and has served on six review committees for the National Academy of Sciences. Breidt has received numerous honors, including recognition with a national prize in environmental statistics, elected membership in the International Statistical Institute, and elected fellowship in the American Statistical Association and the Institute of Mathematical Statistics.

Daniel Cooley – Professor
  • Ph.D., Applied Mathematics, University of Colorado
  • M.S., Applied Mathematics, University of Colorado
  • B.A., Mathematics, University of Colorado

Cooley’s research is primarily in extreme value analysis, which aims to characterize the tail of the distribution. Specifically, his research has focused on describing and modeling extremal dependence both in the multivariate and spatial cases. Much of his research is motivated by quantifying risk associated with extreme weather events and Cooley has collaborated with atmospheric scientists at institutions such as the National Center for Atmospheric Research and the Berkeley National Labs.

Bailey Fosdick – Assistant Professor
  • Postdoctoral Fellow, Statistical and Applied Mathematical Sciences Institute
  • Ph.D., Statistics, University of Washington
  • B.S., Mathematics, Colorado State University

Dr. Fosdick's primary research interests lie in the development of statistical methods for analyzing network data, with particular attention to applications in ecology and the social sciences. She also studies covariance models for multiway data, Bayesian statistics, and methods for survey analysis.

Jennifer Hoeting – Professor
  • Ph.D., Statistics, University of Washington
  • M.S., Statistics, University of Washington
  • B.S., Statistics and Psychology, University of Michigan

Hoeting's research is focused in the areas of Bayesian statistics, spatial statistics, and model selection and uncertainty. Much of her research is aimed at developing new statistical methods to address problems in ecology. Hoeting is the founding editor of the journal Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). Hoeting's book, Computational Statistics (co-authored by Geof Givens), has been adopted as a textbook at top universities in the US and in over 25 other countries. Hoeting has been advisor to more than 30 Ph.D. and M.S. students. Hoeting is an elected Fellow of the American Statistical Association (ASA) and received the Distinguished Achievement Medal from the ASA’s Section on Statistics and the Environment. In 2015, she was named Professor Laureate of the College of Natural Sciences at Colorado State University.

Piotr Kokoszka – Professor

Kokoszka’s research is concerned with statistical modeling and inference. Areas of current interest include: functional data analysis, time series and extreme value theory. Kokoszka serves on several editorial boards, including Journal of the American Statistical Association, Journal of Multivariate Analysis, Scandinavian Journal of Statistics, and Journal of Time Series Analysis. He is a fellow of the Institute of Mathematical Statistics.

Mary Meyer – Professor
  • Ph.D., Statistics, University of Michigan

Meyer’s main area of research is estimation and inference in statistical models with inequality constraints. This includes non-parametric function estimation using constrained regression splines, density and hazard function estimation with shape constraints, models with order restrictions. Meyer has been published in STATISTICAL SCIENCE, the Journal of the Royal Statistical Society, and the Canadian Journal of Statistics.

Aaron Nielsen – Assistant Professor
  • Ph.D., Applied Mathematics, University of Colorado - Denver
  • M.S., Statistics, Colorado State University
  • M.S., Electrical Engineering, University of Colorado - Boulder

Nielsen currently serves as an assistant professor in CSU’s Department of Statistics, specializing in statistics education. He has instructed STAT 201, 204, 301, 315, 460, 472, and 581A4 and STAA 574, in addition to several Math and Stat courses at the University of Colorado.

Ben Shaby – Associate Professor
  • Ph.D., Statistics, Cornell University
  • B.S., Mathematical and Computational Science, Stanford University
  • A.B., English, Stanford University

Shaby develops statistical theory and methods to study extreme weather events and high-throughput biological experiments. He works with climate scientists, hydrologists, and wildfire scientists in academia and government to understand and mitigate the risks associated with rare, high-impact events. Shaby was the recipient of a National Science Foundation CAREER award in 2018 and the American Statistical Association Section on Statistics and the Environment's Early Career Investigator Award in 2016. He completed his Ph.D. at Cornell University in 2009 and held postdoctoral appointments at Duke University and UC Berkeley.

Ben Sharp – Assistant Professor

Julia Sharp – Associate Professor
  • Ph.D., Statistics, Montana State University
  • M.S., Statistics, Montana State University
  • B.S., Mathematics, University of Evansville

Dr. Sharp is an applied statistician with extensive collaborative experience across multiple domains. Her statistics research arises from collaborative projects and includes experimental design, generalized linear mixed models, and categorical data analysis.

Haonan Wang – Professor
  • B.S., Nankai University
  • Ph.D., University of North Carolina - Chapel Hill

Wang’s research includes object-oriented data analysis, functional dynamic modeling, and spatial statistics. His primary research centers on developing new mathematical and statistical methods to solve problems from various scientific research fields. Object oriented data, such as tree-structured data, random graphs, manifold data, and curve data, are frequently collected. In object-oriented data analysis, many problems were motivated by data from neuro-image, which can be represented by a set of tree-structured objects. The fundamental properties of the topological structures were studied. Wang is particularly interested in the problem of modeling tree-structured data to explain the relationship between tree-structured covariates and numerical response, and/or between numerical covariates and tree-structured response. Recently, his research interest centers on the development of novel statistical learning tools for analyzing big data collected from various networks.

Zach Weller – Assistant Professor
  • Ph.D., Statistics, Colorado State University
  • B.A., Mathematics, Concordia College (MN)

Weller works as a statistical consultant in the Graybill Statistics Laboratory. He is broadly interested in applied statistics for solving problems in and related to ecology, environmental health, energy, biology, wildlife management, animal science, and the environment. Weller is also interested in scientific communication, the intersection of science and policy, and statistics education.

Ander Wilson – Assistant Professor
  • Postdoctoral Fellowship, Biostatistics, Harvard T. H. Chan School of Public Health
  • Ph.D., Statistics, North Carolina State University
  • B.A., Mathematics, University of Vermont

Dr. Wilson's research focuses on the development of statistical methodology for the analysis of complex environmental health data with the motivation of better understanding how exposure to air pollution and environmental chemicals influences human health.

Wen Zhou – Assistant Professor
  • Ph.D. in Statistics, Iowa State University
  • Ph.D. in Applied Mathematics, Iowa State University

Dr. Zhou's primary research interests focus on high dimensional inference and statistical machine learning, with particular applications to genomics, genetics, integrative analysis of omics type data, and structural biology. He also pursue researches on graphical modeling, system biology, optimization, and game theory.

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