Stephen Pfohl

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.

Select Publications

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
[abstract] [pdf]

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