I study machine learning for equitable healthcare. My research focuses on two main areas: 1) developing machine learning methods for equitable clinical care, and 2) auditing and addressing algorithmic bias.
I’m an Assistant Professor in UC Berkeley and UCSF’s Computational Precision Health program with a joint appointment in Berkeley EECS. I received my PhD from MIT EECS as a member of the Clinical Machine Learning group. Before MIT, I received a joint AB/SM degree from Harvard University. I have worked at Dropbox and Microsoft Research.
Prospective PhD Students: I am looking for 1-2 PhD students starting Fall 2024. Apply through the Computational Precision Health or EECS admissions portals. Please see my advising statement and lab FAQ for more info. Unfortunately, due to the volume of emails, I cannot respond to or meet with all prospective students.
Machine Learning Approaches for Equitable Healthcare.
Irene Y. Chen.
PhD Thesis, Massachusetts Institute of Technology 2022.
Clustering Interval-Censored Time-Series for Disease Phenotyping.
Irene Y. Chen, Rahul G. Krishnan, David Sontag.
Intimate Partner Violence and Injury Prediction from Radiology Reports.
Irene Y. Chen, Emily Alsentzer, Hyesun Park, Richard Thomas, Babina Gosangi, Rahul Gujrathi, Bharti Khurana.
Ethical Machine Learning in Health Care
Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi.
Annual Reviews for Biomedical Data Science 2021.
Treating health disparities with artificial intelligence
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi
Nature Medicine, January 2020
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph.
Irene Y. Chen, Monica Agrawal, Steven Horng, David Sontag.
Can AI Help Reduce Disparities in General Medical and Mental Health Care?
Irene Y. Chen, Peter Szolovits, Marzyeh Ghassemi.
AMA Journal of Ethics, February 2019.
Why Is My Classifier Discriminatory?
Irene Y. Chen, Fredrik D. Johansson, David Sontag.
Spotlight Presentation (top 4% of submitted papers)
Presented at WiML workshop at NeurIPS 2017.