Stephen A. Walsh

Hi, I'm Steve, Assistant Professor of Data Science at Elms College.

My current research focuses on uncertainty quantification for computer model output through Bayesian hierarchical modeling paired with Gaussian processes. I'm also interested in applications related to spatial and environmental statistics and statistical collaboration. I received my PhD in Statistics in June 2023 from Virginia Tech with advisors Marco Ferreira and Dave Higdon. I enjoy furthering data science education and developing the Data Science major at Elms.

Selected work

Walsh, S. A., Booth, A. S., Higdon, D., Clark, J., Moran, K. R., & Heitmann, K. (2026). Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra. Data Science in Science 5(1), pp. 1-14. (link, arXiv)

Walsh, S. A., Ferreira, M.A.R., Higdon, D., & Zick, S. (2023). A Bayesian Hierarchical Model Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts. Annals of Applied Statistics 17(3), pp. 1984-2001 (link, arXiv).

Higdon, D. & Walsh, S.A. (2022). Discussion of paper by Marmin & Filippone. A discussion of "Deep Gaussian Processes for Calibration of Computer Models" by S. Marmin & M. Filippone. Bayesian Analysis 17(4), pp. 1301-1350 (link).

GitHub Repository

My GitHub repo can be found at https://github.com/stevewalsh124, which has code related to my projects listed above. If you have any questions, feel free to email me.

Teaching Experience

At Elms, I am currently developing the new Data Science and AI major and teaching a Computational Statistics course which introduces statistics alongside coding experience with R. I am also teaching a Data Analytics course using Python, as well as the Data Analytics capstone course.

During my time at Virginia Tech, I had the opportunity to teach STAT 4714: Probability & Statistics for Electrical Engineers, an undergraduate statistics course covering the concepts of probability, random variables, distributions, estimation, and hypothesis testing with emphasis on applications in electrical engineering. I spoke with Engineering professors at VT to learn about how the material in this course could be modified to best prepare the students for subsequent classes (this resulted in changing the coding language from R to MATLAB, as well as including lectures related to the Weibull distribution and stochastic processes).

I have also taught short courses through the Statistical Applications and Innovations Group (SAIG) at Virginia Tech. The two hour courses I have instructed include Simple Linear Regression in R, t-tests in R, Basics of R, and Data Manipulation in R. I have also volunteered as a computing mentor through the SOARS program, and I have five years of mathematics teaching experience at Pathfinder Tech, where I established a curriculum for a Personal Finance elective course and taught numerous other courses (including Honors and AP classes).

Honors and Awards

Virginia Tech: Jesse Arnold Award for outstanding departmental teaching; 2022
Virginia Tech: Klaus Hinkelmann Award for outstanding departmental service; 2020 & 2022
Virginia Tech: Institute for Critical Technologies & Applied Science (ICTAS) Doctoral Scholar Fellowship; 2017-2021
Mu Sigma Rho Statistics Honors Society Inductee; 2019
Westfield State University: University Honors; 2012
Westfield State University: Academic Excellence Award in Mathematics; 2012

Stephen Walsh

Division of Natural Sciences,

Mathematics & Technology

Lyons Center, Elms College

291 Springfield St

Chicopee, MA 01013

walshst@elms.edu

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