I am an assistant professor at University of Michigan’s Department of Biostatistics. My work focuses on improving healthcare through:
- developing statistical methods with emphasis on predictive modeling and interpretability (often inspired by and leveraging advances in AI)
- creating well-documented, user-friendly software, and
- teaching statistics for biomedical applications in a friendly, accessible way.
My research applications are most often in cancer and immunology, but I am generally driven to solve problems that will help people.
I earned a PhD in Biomedical Data Science from Stanford, where I was advised by Rob Tibshirani. I also hold a BA in Mathematics and an MS in Data Science from New College of Florida, where I was advised by Pat McDonald and Gary Kalmanovich.
Before my PhD, I led the math content team at Wolfram|Alpha. Notably, we developed Step-by-step Solutions and the Wolfram Problem Generator.
Want to collaborate? Please reach out at ercr@umich.edu.
Selected publications
Craig, Pilanci, Le Menestrel, Narasimhan, Rivas, Gullaksen, Dehghannasiri, Salzman, Taylor, Tibshirani. Pretraining and the lasso. Journal of the Royal Statistical Society, Series B, 2026. doi
Zaslavsky, Craig, et al. Disease diagnostics using machine learning of B cell and T cell receptor sequences. Science, 387(6736), 2025. doi
Craig, Keyes, et al. Annotation-free discovery of disease-relevant cells in single-cell datasets. Science Advances, 11(35), 2025. doi
Le Menestrel, Craig, Tibshirani, Hastie, Rivas. Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the UK Biobank. PLoS One, 20(12), 2025. doi
Craig, Zhong, Tibshirani. A review of survival stacking: a method to cast survival regression analysis as a classification problem. International Journal of Biostatistics, 21(1), 2025. doi
Hamilton, Craig, et al. CAR19 monitoring by peripheral blood immunophenotyping reveals histology-specific expansion and toxicity. Blood Advances, 8(12), 2024. doi
Full list of publications, talks, and grants in my CV and on Google Scholar.
Software
ptLasso. Pretraining for the lasso — transfer learning for sparse, interpretable linear models. Video and code tutotials!
MMIL. Mixture models for multiple-instance learning: find disease-relevant cells from patient-level labels.
sweetspot. Find and assess treatment-effect sweet spots in clinical trials.
Contact