I am PhD student in Biomedical Informatics at Stanford, advised by Nigam Shah. My work broadly focuses on the use of machine learning to guide clinical decision making. Recently, I have focused on understanding how to design such systems to be fair and robust. Before coming to Stanford, I worked with Cassie Mitchell at Georgia Tech on developing models of disease progression in Amyotrophic Lateral Sclerosis. In the summer of 2019, I was a research intern at Google Health.
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah.
Journal of Biomedical Informatics, 10.1016/j.jbi.2020.103621, 2020
[paper] [pre-print] [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