Irene Y. Chen
Lab Group - Bio - Resources - Reading List - Blog

I am an Assistant Professor at UC Berkeley and UCSF in Computational Precision Health and EECS as well as a faculty member of Berkeley AI Research (BAIR). I am interested in how we can make machine learning systems for healthcare to be more robust, impactful, and equitable. In particular, I develop computational tools for statistical inference with ML models, noisy data-constrained settings, and addressing algorithmic bias.

I received my PhD from MIT EECS and my joint AB/SM in Applied Math from Harvard University. Previously, I was a postdoctoral researcher at Microsoft Research New England, a data scientist at Dropbox, and an enumerator for the US Census Bureau.

Prospective PhD Students: The CHEN lab is looking for 1-2 PhD students starting Fall 2025. Apply through the Computational Precision Health or EECS (AI-H) admissions portals. Please see my advising statement and lab FAQ for more info.

Contact Info


Twitter: @irenetrampoline

Github: irenetrampoline

Google Scholar


July 2023: I have started as an assistant professor at UC Berkeley and UCSF!

Aug 2022: I have successfully defended my thesis and submitted my dissertation!

Dec 2021: Our paper on clustering interval-censored time-series for disease phenotyping was accepted to AAAI 2022. Our paper on biases in health insurer analytics was accepted to Health Affairs.

Dec 2021: Talk and panel discussion at Stanford AI + Health; spoke at two NeurIPS workshops on human and machine decisions and causal reasoning for fairness

Oct 2021: Our paper on underdiagnosis bias in chest radiograph algorithms was accepted to Nature Medicine.

Apr 2021: Podcast interview on the TWiML AI Podcast

Aug 2020: I finished my wonderful internship at Microsoft Research NYC, hosted by Solon Barocas and Hal Daumé III

Apr 2018: Ran the Boston Marathon in 38 degree weather

Organizing Committees

CHIL 2024 Program Chair

NeurIPS 2023 Comms Chair

Toward Algorithmic Justice in Precision Medicine 2023 Advisory Committee

FAccT 2023 Doctoral Colloquium Chair

ML4H Symposium 2022 General Chair

CHIL 2021 Tutorials Chair

CHIL 2020 Tutorials Chair

CHIL 2020 Track Chair

Fair ML for Health Workshop at NeurIPS 2019 Founder and Co-Chair

ML4H Workshop at NeurIPS 2020 Organizer

ML4H Workshop at NeurIPS 2019 Organizer

ML4H Workshop at NeurIPS 2018 Organizer


Rising Star in AI - Harvard CRCS 2021

Rising Star in EECS - University of California Berkeley 2021

Rising Star in ML - University of Maryland 2021

Neurips Top 400 Reviewer 2019

Seth J. Teller Award for Excellence, Inclusion, and Diversity 2018

PD Soros Fellowship Finalist 2018

Derek Bok Certificate of Distinction in Teaching 2011

Selected Papers (Show all)

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.
AAAI 2022.

Intimate Partner Violence and Injury Prediction from Radiology Reports.
Irene Y. Chen, Emily Alsentzer, Hyesun Park, Richard Thomas, Babina Gosangi, Rahul Gujrathi, Bharti Khurana.
PSB 2021.
Oral Presentation.

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.
PSB 2020.
Oral Presentation.

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.
NeurIPS 2018.
Spotlight Presentation (top 4% of submitted papers)
Presented at WiML workshop at NeurIPS 2017.