I am an Assistant Professor in the Department of Statistics at Texas A&M University. Previously, I was a postdoctoral associate in the Department of Statistics and Data Science at Cornell University working with Dr. David Matteson. At Cornell, I coordinated research activities associated with the NSF funded PRISM Institute for Trans-domain Systemic Risk. I finished my PhD in 2020 at the University of Missouri advised by Dr. Christopher Wikle. My quantitative research interests span many topics, but with an underlying commonality that includes spatio-temporal modeling, Bayesian statistics, and alternative learning frameworks such as machine learning, deep learning, and reinforcement learning. I am highly motivated by scientific hypotheses with an emphasis on ecological and environmental applications.
Prospective students: Students with an interest in ecological data science and/or spatiotemporal Bayesian statistics are encouraged to apply to the PhD program in Statistics.
News
- October 2026: Donald Turner III started a full-time position as a Statistical Scientist at Exponent.
- October 2026: Wrapped up the three-year Sandia National Labs project on machine learning for data-driven closure models in Earth systems.
- September 2026: New paper led by former postdoc Benjamin Hoose in The Journal of Wildlife Management, “Landscape drivers of scaled quail occurrence across a broad spatiotemporal extent.”
- September 2026: New preprint, “Quantifying temporal instability in ecological assemblages using modern portfolio theory,” now under review at Ecography.
- July 2026: Released version 1.6.0 of the R package dsp.
- July 2026: Awarded a Texas A&M AI Bridge Seed Grant with Cheng Zhang (Engineering) for “Bridging the Gap in Wildlife Monitoring: A Hybrid AI-Ecological Approach for Cryptic Taxa Detection and Multi-Source Data Fusion.”
- June 2026: New paper in Spatial Statistics, “A multi-stage Bayesian approach to fit spatial point process models,” with Ran Ren and Mevin Hooten.
- May 2026: Donald Turner III, co-advised with Dr. Yang Ni, graduated with his Ph.D. in Statistics from Texas A&M. Congratulations, Donald!
- May 2026: Postdoc Benjamin Hoose started a position at the Texas A&M Natural Resources Institute.
- February 2026: New paper led by John Paul Acosta in Environmental and Ecological Statistics on machine learning interpolation of movement trajectories.
- August 2025: Released version 1.2.0 of the R package dsp for dynamic shrinkage processes and changepoint detection.
- May 2025: Elizabeth Chun graduated with an M.S. in Statistical Data Science from Texas A&M.
- January 2025: Began serving as Secretary of the ASA Section on Statistics and the Environment.
- November 2024: New paper in Technometrics, “Locally adaptive shrinkage priors for trends and breaks in count time series,” with David Matteson.
- July 2024: Two new changepoint papers with Haoxuan Wu and David Matteson, “Drift vs shift” in Technometrics and “Trend and variance adaptive Bayesian changepoint analysis” in the Journal of Business & Economic Statistics.
- February 2024: Received a supplement from the National Park Service to continue work on cruise ship visitation in Johns Hopkins Inlet.
- January 2024: Elected Treasurer of the ASA Section on Statistics and the Environment, a position that continues as Secretary the following year.
- January 2024: Renewed as a ConocoPhillips Data Science Faculty Fellow for a second year.
- October 2023: Awarded a grant from Sandia National Labs, “Machine Learning for Data-Driven Closure Models in Earth Systems.”
- September 2023: Awarded a grant from the National Park Service to assess cruise ship visitation in Johns Hopkins Inlet.
- March 2023: New paper in Methods in Ecology and Evolution, “Clustering future scenarios based on predicted range maps.”
- February 2023: New papers in Scientific Reports on location and acceleration data for animal behavior and in Data Science in Science on renewable energy and electricity prices.
- January 2023: Named a ConocoPhillips Data Science Faculty Fellow.
- Fall 2022: Joined the Department of Statistics at Texas A&M University as an Assistant Professor.
- June 2022: New paper in The Annals of Applied Statistics, “Bayesian inverse reinforcement learning for collective animal movement.”
Interests
Spatio-temporal statistics · Reinforcement learning · Bayesian statistics · Machine learning · Ecology · Energy · Sports
Education
Ph.D. in Statistics, University of Missouri, 2020
M.A. in Statistics, University of Missouri, 2018
B.S. in Statistics & Wildlife Biology, Colorado State University, 2014