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I’m a Ph.D. student in computer science at MIT, advised by David Sontag in the Clinical Machine Learning group. I work on machine learning methods to advance understanding of health and reduce inequality. Prior to MIT, I completed a joint AB/SM degree at Harvard. I also worked at Dropbox as a data scientist, machine learning engineer, and chief of staff.

For the summer of 2020, I interned at Microsoft Research NYC with the FATE group advised by Solon Barocas and Hal Daumé III.

You can reach me at iychen [at] mit [dot] edu or on Twitter.



Clustering Censored Multivariate Time-Series.
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.
PSB 2021, Oral Presentation.

CheXclusion: Fairness gaps in deep chest X-ray classifiers.
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y. Chen, Marzyeh Ghassemi.
PSB 2021, Oral Presentation.

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.

Why Is My Classifier Discriminatory?
Irene Y. Chen, Fredrik D. Johansson, David Sontag.
NeurIPS 2018, Spotlight Presentation (top 4% of submitted papers).
[abstract, pdf, slides, poster]

Sources of Unfairness in Intensive Care Unit Mortality Scores.
Irene Y. Chen, Fredrik D. Johansson, David Sontag.
Women in Machine Learning Workshop at NeurIPS 2017.


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.

Probabilistic Machine Learning for Healthcare
Irene Y. Chen*, Shalmali Joshi*, Marzyeh Ghassemi, Rajesh Ranganath.
Annual Reviews for Biomedical Data Science, 2021.

Treating health disparities with artificial intelligence
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi
Nature Medicine, January 2020.

Trends and Focus of Machine Learning Applications for Health Research
Brett Beaulieu-Jones, Samuel G. Finlayson, Corey Chivers, Irene Y. Chen, Matthew McDermott, Jaz Kandola, Adrian V. Dalca, Andrew Beam, Madalina Fiterau, Tristan Naumann
JAMA Network Open, October 2019.

Turning the crank for machine learning: ease, at what expense?
Tom J. Pollard, Irene Y. Chen, Jenna Wiens, Steven Horng, Danny Wong, Marzyeh Ghassemi, Heather Mattie, Emily Lindmeer, Trishan Panch.
Lancet Digital Health, September 2019.

Practical guidance on artificial intelligence for health-care data.
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath.
Lancet Digital Health, August 2019.

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.


We should treat algorithms like prescription drugs.
Andy Coravos, Irene Chen, Ankit Gordhandas, Ariel Dora Stern.
Quartz, February 14, 2019.


At MIT, I served as a Teaching Assistant in Spring 2019 for Machine Learning for Healthcare. Our class was covered by MIT News.

At Harvard, I was awarded the Derek Bok Center Certificate of Distinction in Teaching for outstanding teaching evaluations. I served on the teaching staff of the following Harvard classes:

Selected Press

MIT News, “Want to know what software-driven health care looks like? This class offers some clues”, Kim Martineau, July 24, 2019.

MIT News, “The Heart of the Matter”, Lillie Paquette, May 10, 2019.

NPR / WGBH, “Fixing Bias In Algorithms Is Possible, And This Scientist Is Doing It”, Heather Goldstone and Elsa Partan, Dec 9, 2018.


In my free time, I enjoy long distance running, reading books, and discussing AI ethics.