Brandon M. Greenwell
greenwell.brandon@gmail.com · (614) 288-9450 · github.com/bgreenwell
Education
- Ph.D., Applied Mathematics — Air Force Institute of Technology, WPAFB, OH, 2014 (GPA 4.0)
Dissertation: “Topics in Statistical Calibration”
- M.S., Applied Statistics — Wright State University, Dayton, OH, 2011 (GPA 4.0)
- B.S., Statistics — Wright State University, Dayton, OH, 2009
Employment
Director, Data Science — 84.51° / Kroger, Cincinnati, OH (2022–present)
[TODO: 2–3 bullets describing the Director role — technical scope, team, strategic impact]
Lead Data Scientist — 84.51° / Kroger, Cincinnati, OH (2018–2022)
- Led the Enable the Science (ETS) team; drove ML/statistical best practices across 200+ data scientists via training, internal tooling, and screencasts
- Built and maintained internal R, Python, and Spark libraries; co-led a machine learning interpretability special interest group
- Contributed to a COVID-19 forecast adjustment effort; supported Kroger optimization team on predictive maintenance
Adjunct Instructor — University of Cincinnati, Carl H. Linder College of Business (2018–present)
Graduate-level statistics: Applied Linear Regression (BANA 7052), Advanced Statistical Modeling (BANA 7042).
Adjunct Professor — Wright State University, Dept. of Mathematics and Statistics, Dayton, OH (2017–2022)
Graduate-level statistics: Biostatistics (STT 6300), Environmental Statistics (STT 7140).
Data Scientist — Wright-Patterson AFB, OH (2015–2017)
Contracted to the U.S. Air Force School of Aerospace Medicine and AFIT. Analyzed observational medical data; developed R packages; delivered training in statistics, data mining, and R programming; provided statistical consultation for aerospace and occupational medicine research.
Associate Research Engineer — Aptima, Inc., Dayton, OH (2014–2015)
Applied statistical and machine learning methods for client engagements; wrote proposals; provided statistical consultation.
Postdoctoral Researcher — Dept. of Mathematics and Statistics, AFIT (2014)
Proposed new methods for inverse estimation with complex data structures, nonparametric Bayesian inference for hierarchical reliability data, and classification tree-based sequential testing thresholds.
Books
Journal Articles
- Greenwell, B.M. (under review). “Fitting Explainable Boosting Machines in R with the ebm Package.” The R Journal.
- Zhu, X., Lin, Z., Liu, D., and Greenwell, B.M. (2025). “SurrogateRsq: An R Package for Categorical Data Goodness-of-Fit Analysis Using the Surrogate R².” The New England Journal of Statistics in Data Science, 3(1), 94–105. DOI
- Greenwell, B.M., Dahlmann, A., and Dhoble, S. (2023). “Explainable Boosting Machines with Sparsity: Maintaining Explainability in High-Dimensional Settings.” arXiv:2311.07452
- Liu, D., Zhu, X., Greenwell, B.M., and Lin, Z. (2022). “A New Goodness-of-Fit Measure for Probit Models: Surrogate R².” The British Journal of Mathematical and Statistical Psychology, 76, 192–210.
- Fountain-Jones, N.M., Kozakiewicz, C.P., Forester, B.R., Landguth, E.L., Carver, S., Charleston, M., Gagne, R.B., Greenwell, B., et al. (2021). “MrIML: Multi-Response Interpretable Machine Learning to Model Genomic Landscapes.” Molecular Ecology Resources, 21, 2766–2781. DOI
- Greenwell, B.M., McCarthy, A.J., Boehmke, B.C., and Liu, D. (2020). “Variable Importance Plots: An Introduction to the vip Package.” The R Journal, 12(1), 343–366. Link
- Greenwell, B.M., Tvaryanas, A.P., and Maupin, G.M. (2018). “Risk Factors for Hearing Decrement Among U.S. Air Force Aviation-Related Personnel.” Aerospace Medicine and Human Performance, 89(2), 80–86. DOI
- Tvaryanas, A.P., Greenwell, B.M., Vicen, G.J., and Maupin, G.M. (2018). “The Commander’s Wellness Program: Assessing the Association Between Health Measures and Physical Fitness Assessment Scores.” Military Medicine. DOI
- Greenwell, B.M., McCarthy, A.J., and Boehmke, B.C. (2018). “A Simple and Effective Model-Based Variable Importance Measure.” arXiv:1805.04755
- Greenwell, B.M., McCarthy, A.J., Boehmke, B.C., and Liu, D. (2018). “Residuals and Diagnostics for Binary and Ordinal Regression Models: An Introduction to the sure Package.” The R Journal, 10(1), 381–394. Link
- Greenwell, B.M. (2017). “pdp: An R Package for Constructing Partial Dependence Plots.” The R Journal, 9(1), 421–436. Link
- Schubert, C.M., Greenwell, B.M., DeSimio, M.P., and Derriso, M.M. (2015). “The Probability of Detection for SHM Systems: Use of Repeated Measures Data.” International Journal of Structural Health Monitoring, 14(3), 252–264.
- Greenwell, B.M. and Schubert Kabban, C.M. (2014). “investr: An R Package for Inverse Estimation.” The R Journal, 6(1), 90–100.
Software
| Package |
Since |
Role |
Description |
| fastshap |
2019 |
Author/maintainer |
Fast approximate Shapley values in R via C++/Rcpp |
| gbm |
2018 |
Maintainer |
Original open-source gradient boosting machine in R |
| vip |
2017 |
Author/maintainer |
Variable importance plots; used in tidymodels ecosystem |
| sure |
2017 |
Author/maintainer |
Surrogate residuals and diagnostics for ordinal regression |
| pdp |
2016 |
Author/maintainer |
Partial dependence plots; CRAN task view: Machine Learning |
| bpa |
2016 |
Author/maintainer |
Basic pattern analysis for data cleaning |
| ramify |
2016 |
Author/maintainer |
Extended matrix utilities for R (Julia/MATLAB-style) |
| investr |
2013 |
Author/maintainer |
Inverse estimation; CRAN task view: Chemometrics |
Talks
Invited
- (2023) “Interpretable Machine Learning.” R-Ladies, Den Bosch and Utrecht. March 2023. Slides
- (2021) “Scalable Shapley Explanations in R.” satRday, Columbus, OH. October 2021. Slides
- (2018) “Random Forests and Gradient Boosting Machines in R.” Machine Learning Day, Linder College of Business, University of Cincinnati. February 2018. Materials
- (2012) “Random Forests.” Department of Mathematics and Statistics, Wright State University. February 2012.
Contributed
- (2024) “DAG Nammit: The Challenges and Dangers of Causally Interpreting Machine Learning Models.” UC Data Science Symposium, Cincinnati, OH. Video
- (2016) “Analysis of Noise-Induced Hearing Loss in U.S. Air Force Aviation-Related Special Duty Personnel.” Aerospace Medical Association 87th Scientific Meeting.
- (2014) “Inverse Estimation with Random Coefficient Models and Its Implementation in R.” Joint Statistical Meetings, Boston, MA.
Teaching
| Course |
Title |
Institution |
Years |
| BANA 7042 |
Advanced Statistical Modeling |
University of Cincinnati |
2022– |
| BANA 7052 |
Applied Linear Regression |
University of Cincinnati |
2018– |
| STT 7140 |
Environmental Statistics |
Wright State University |
2018 |
| STT 6300 |
Biostatistics |
Wright State University |
2017 |
| STAT 645a |
Bayesian Inference |
Air Force Institute of Technology |
2014 |
Service & Professional Activities
Accreditations
- Accredited Graduate Statistician (GStat), American Statistical Association, 2014. ASA
Professional Organizations
- American Statistical Association (2012–)
- Cincinnati/Dayton R User Group — CinDay RUG (2013–)
ASA Leadership
- President, Dayton Area Chapter (2014–2017)
- Vice President, Dayton Area Chapter (2012–2014)
Peer Review
- The R Journal
- Journal of Modern Applied Statistical Methods (JMASM)
Awards
- Wright State University Graduate Student Excellence Award, M.S. in Applied Statistics, 2011