I am a research scientist at Google Research and a recent graduate of the Biomedical Informatics PhD program at Stanford University. At Stanford, my work focused on understanding fairness, robustness, and transparent evaluation of systems that use machine learning to inform clinical decision making.
Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation
Agata Foryciarz, Stephen R. Pfohl, Birju Patel, Nigam H. Shah
medRxiv 2021.11.08.21266076; doi: https://doi.org/10.1101/2021.11.08.21266076
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah.
arXiv preprint arXiv:2108.12250, 2021
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah.
Journal of Biomedical Informatics, 113:103621, 2021
[paper] [preprint] [code]
Counterfactual Reasoning for Fair Clinical Risk Prediction
Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah.
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:325-358, 2019
Creating fair models of atherosclerotic cardiovascular disease risk
Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H Shah.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019
[arxiv post-print with erratum] [pdf] [paper]
Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction
Stephen R. Pfohl, Renaid B. Kim, Grant S. Coan, Cassie S. Mitchell.
Frontiers in Neuroinformatics, 12:36 2018
Characterization of the contribution of genetic background and gender to disease progression in the SOD1 G93A mouse model of amyotrophic lateral sclerosis: a meta-analysis
Stephen R. Pfohl, Martin T. Halicek, Cassie S. Mitchell.
Journal of Neuromuscular Diseases, 2(2):137–150, 2015