About Me
I am beginning in Fall 2023 as an Assistant Professor in the Department of Statistics at Purdue University. I received my PhD in Statistics at the University of Michigan in 2022 studying under the supervision of Tailen Hsing and Stilian Stoev. I spent one year as a postdoc at Sandia National Laboratories from 2022-2023. My research focuses on environmental applications of statistics, especially in oceanography and climate, as well as some broader interests:
spatial statistics/multivariate processes
functional data analysis
climate model calibration
changepoint analysis
dimension reduction, unsupervised learning, exploratory data analysis, scientific computing
statistics and public policy
Teaching
At Purdue University:
Stat 417: Statistical theory (Fall 2023, Spring 2024)
Graduate student instructor at University of Michigan:
Stats 250: Introductory statistics (Fall 2017)
Stats 425: Upper-level undergraduate probability (Winter 2018)
Stats 620: PhD-level course on stochastic processes and Markov chains (Winter 2021)
Other teaching (University of Michigan):
August Math Preparation Workshop Instructor: a thirty-hour, week-long course split between two instructors for incoming PhD and Masters students (August 2021)
Fall Prep Workshop Instructor: a nine-hour course on upper-level undergraduate probability and statistics for incoming Masters and PhD students (August 2020)
Manuscripts
Yarger, D., Bhadra, A. "On valid multivariate generalizations of the Confluent Hypergeometric covariance function." https://arxiv.org/abs/2312.05682. Submitted.
Yarger, D., Stoev, S., Hsing, T. "Multivariate Matern models---A spectral approach." https://arxiv.org/abs/2309.02584. Minor revisions, Statistical Science.
Yarger, D., Wagman, B., Chowdhary, K., Shand, L. "Autocalibration of the E3SM Version 2 atmosphere model using a PCA-based surrogate for spatial fields." https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023MS003961, https://arxiv.org/abs/2308.06877. Journal of Advances in Modeling Earth Systems.
Yarger, D., Tucker, J. D. "Detecting changepoints in globally-indexed functional time series." https://arxiv.org/abs/2308.05915. Submitted to Spatial Statistics.
Tucker, J. D., Yarger, D. "Elastic functional changepoint detection." https://doi.org/10.1002/env.2826. Environmetrics.
Hansen, D., Yarger, D. "ArgoSSM: A state-space model of ocean floats under ice." https://arxiv.org/abs/2210.00118
Korte-Stapff, M.*, Yarger, D.*, Stoev, S., Hsing, T. "A functional-data mixture model for cokriging multivariate spatial data." https://arxiv.org/abs/2211.04012 * denotes equal contribution.
Yarger, D., Stoev, S., and Hsing, T. (2021). "A functional-data approach to the Argo data.'' Annals of Applied Statistics.
Code available at https://github.com/dyarger/argofda
Accompanying R Shiny Applications linked at https://sites.google.com/a/umich.edu/argostatistics/home/fdapaper
Arxiv link: https://arxiv.org/abs/2006/05020
Other activities
Mentor, University of Michigan Statistics Directed Reading Program (Fall 2020 - Winter 2021)
Volunteer, University of Michigan Statistics in the Community (STATCOM) (Fall 2019 - Summer 2020)
Projects
Policy Analytics internship, Federal Reserve Board of Governors (Summer 2017)
Junior Fellow, Joint Program in Survey Methodology, Improving Motor Carrier Safety Measurement, National Academy of Sciences, Engineering, and Medicine (Summer 2016)
Center for Interdisciplinary Research, St. Olaf College, Assessing Professional Learning Communities (2015-2016)
Education
Ph.D. Candidate, Statistics, University of Michigan (2019-2022)
Ph.D. Precandidate, Statistics, University of Michigan (2017-2019)
B.A. St. Olaf College, Mathematics Major, Concentrations in Statistics and Linguistics (2013-2017)
Contact
anyarger, purdue.edu, dyarger, umich.edu
