Lucas Mentch is an Associate Professor in the Department of Statistics at the University of Pittsburgh. He obtained is PhD from Cornell University in 2015 and an undergraduate degree in mathematics from Bucknell University in 2010. He has published dozens of scientific articles and book chapters at the intersection of statistical inference and machine learning and applied to these methodologies to a diverse array of application areas ranging from ecology to medicine to sports statistics to law, policing, and forensic science.
Courses
STAT 1361/2360: Statistical Learning and Data Science
- Ph.D., Statistics, Cornell University (2015)
- M.S., Statistics, Cornell University (2013)
- B.S., Mathematics, Bucknell University (2010)
Education & Training
Mentch, L., & Hooker, G. (2016). Quantifying uncertainty in random forests via confidence intervals and hypothesis tests. Journal of Machine Learning Research, 17(26), 1-41.
Mentch, L., & Zhou, S. (2020). Randomization as regularization: A degrees of freedom explanation for random forest success. Journal of Machine Learning Research, 21(171), 1-36.
Mentch, L. (2020). On racial disparities in recent fatal police shootings. Statistics and Public Policy, 7(1), 9-18.
Coleman, T., Peng, W., & Mentch, L. (2022). Scalable and efficient hypothesis testing with random forests. Journal of Machine Learning Research, 23(170), 1-35.
Kissel, N., & Mentch, L. (2024). Forward stability and model path selection. Statistics and Computing, 34(2), 82.
- Statistical Learning
- Machine Learning Inference
- Variable Importance
- Model Set Selection / Rashomon Sets
