Irene Y Chen

Irene Y. Chen is an Assistant Professor at the University of California, Berkeley and UCSF in the Computational Precision Health (CPH) program, with a joint appointment in the Department of Electrical Engineering and Computer Science (EECS). Her research focuses on developing machine learning methods to improve clinical care equity, audit algorithmic models, and address biases in healthcare AI systems. She holds a PhD from MIT (EECS) and a joint AB/SM in Applied Math from Harvard University. Previously, she was a postdoctoral researcher at Microsoft Research New England, a data scientist at Dropbox, and an enumerator for the US Census Bureau.

• Affiliations: UC Berkeley EECS, UCSF CPH, Berkeley AI Research (BAIR)
• Education: PhD in EECS (MIT, 2022), AB/SM in Applied Math (Harvard, 2014)

Her research interests include robust AI systems for healthcare, bias mitigation in algorithms, and translational machine learning for clinical decision-making. She has published in top venues like NeurIPS, AAAI, Nature Medicine, and Lancet Digital Health. Her work has been recognized with awards including the Google Research Scholar (2024), Best Findings Paper at ML4H 2024, and the Rising Star in EECS (2021).

Awards include grants from Apple Machine Learning Research (2024) and Google Faculty Research Award (2024). She actively organizes conferences like NeurIPS and chairs committees for health equity in AI.

Her lab focuses on collaborative projects addressing disparities in healthcare access and algorithmic fairness. Key collaborations include work on liver transplant decision-making, maternal health guidelines, and treatment adherence bias analysis.