Our team consists of post-docs, students, research scientists, and clinical collaborators. For information about how to join us, see our FAQ section.

Current Members

Current Members

David Sontag

David Sontag is an Associate Professor of Electrical Engineering and Computer Science at MIT, part of both the Institute for Medical Engineering & Science and the Computer Science and Artificial Intelligence Laboratory. His research focuses on advancing machine learning and artificial intelligence, and using these to transform health care. Previously, he was an Assistant Professor of Computer Science and Data Science at New York University, part of the CILVR lab.

Rahul G. Krishnan

Rahul G. Krishnan is a PhD student at MIT. He holds a B.Eng. in Computer Engineering from the University of Toronto and an MS from New York University. His research interests include probabilistic inference in deep generative models and building new machine learning algorithms for modeling disease progression, patient similarity and treatment efficacy.

Irene Chen

Irene Chen is a PhD student in Electrical Engineering and Computer Science. Before MIT, she received a joint AB/SM degree from Harvard in applied math and computational science where she conducted research on discrimination on Airbnb. Her research interests focus on robust machine learning. Her projects include learning disease progression, extracting medical knowledge, and treating health disparities.

Mike Oberst

Michael Oberst is a Computer Science PhD Student at MIT. He holds a BA in Statistics from Harvard University, where he was advised by Edo Airoldi on his senior thesis. His research interests include developing learning algorithms for dealing with non-stationarity / dataset shift in predictive modelling, as well as robust learning of treatment policies from observational data.

Monica Agrawal

Monica Agrawal is a PhD student in Computer Science at MIT. She holds a B.S. and M.S. in Computer Science from Stanford University, where she was advised by Jure Leskovec and worked on applying machine learning to biological networks. Her research interests include interpretability in machine learning and clinical natural language processing.

Zeshan Hussain

Zeshan Hussain is an MD/PhD student at Harvard/MIT. He holds a B.S. and M.S. in Computer Science from Stanford University, where he was advised by Daniel Rubin and Chris Re, doing work on data augmentation, medical image classification, and deep learning. Currently, his research interests include interpretability of non-linear models, specifically RNNs, as well as disease progression modeling.

Rebecca Peyser Boiarsky

Rebecca Peyser Boiarsky is a PhD student in Computer Science at MIT. She holds an M.S. in Biomedical Engineering from Columbia University and a B.A. in Physics with a minor in Computer Science from Yeshiva University. Before joining the lab, she worked as a Bioinformatics Analyst at Regeneron Pharmaceuticals. Her research interests include developing methods to learn disease subtypes and disease progression models for precision medicine applications. She is particularly interested in analyzing genomic data to better understand disease.

Christina Ji

Christina Ji is a Ph.D. student in Computer Science at MIT. She is interested in examining the theoretical assumptions behind off-policy evaluation of reinforcement learning for healthcare and developing algorithms for disease progression modeling.

Matt Alkaitis

Matt is an MD/PhD student at Harvard Medical School. He received his PhD in biochemistry from Oxford University as a member of the NIH MD/PhD Graduate Partnership Program. He also holds a BA in biology from Dartmouth College. His research interests include clinical natural language processing and few shot learning applied to oncology.

Hussein Mozannar

Hussein Mozannar is a PhD student in Social & Engineering Systems and Statistics. He received his undergraduate degree in computer engineering from the American University of Beirut. His current research interests focus on human-centric aspects of machine learning, namely how to integrate expert decision makers into machine learning pipelines while ensuring fairness and an understanding of long-term consequences.

Chloe O'Connell

Chloe O'Connell is currently completing her intern year at Newton Wellesley Hospital as part of an anesthesiology residency at Massachusetts General Hospital. Prior to residency, she completed medical school at Stanford, where she also completed a master's degree in Biomedical Informatics. She is interested in clinical natural language processing and how it can be used to improve the ease and efficiency of healthcare documentation while at the same time improving the quality of information recorded.

Divya Gopinath

Divya Gopinath is an MEng student with the group and studied computer science for her bachelors at MIT. Her research focuses on using machine learning and causal inference to build intelligent systems for healthcare.

Sooraj Boominathan

Sooraj Boominathan is an MEng student who previously studied computer science and mathematics as an undergrad at MIT. His research involves predictive modeling of individual patient antibiotic resistance profiles using medical data.

Rohan Kodialam

Rohan Kodialam is an MEng student working with the Clinical ML group. He previously studied Physics and Computer Science at MIT. His research with the group focuses on developing algorithms for predictive modelling using time-series data, and his other research interests include augmenting classical algorithms with machine learning.

Ariel Levy

Ariel Levy is an undergraduate at MIT studying computer science. She is working on the creation of a Natural Language Processing powered platform to structure datasets of annotated Electronic Health Record notes.

Elizabeth Han

Elizabeth Han is an undergraduate at MIT studying mathematics and computer science. Her research involves developing robust machine learning models for high-dimensional medical data.

Justin Lim

Justin Lim is a an undergraduate at MIT pursuing a degree in mathematics and computer science. His research focuses on learning treatment policies that are optimal, dynamic, and interpretable.

Shreyas Balaji

Shreyas Balaji is an undergraduate at MIT pursuing a degree in mathematics, with some computer science and physics. His research focuses on developing algorithms to handle non-stationarity in data.


Name Lab Role Post-lab
Fredrik Johansson Postdoc Assistant Professor, Chalmers University of Technology
Anastasia Podosinnikova Postdoc
Narges Razavian Postdoc Assistant Professor, NYU School of Medicine
Uri Shalit Postdoc Assistant Professor, Technion
Sanjat Kanjilal Clinical Fellow Lecturer, Harvard Pilgrim Health Care Institute
Rachel Hodos PhD student Benevolent AI
Yacine Jernite PhD student Facebook AI Research
Yoni Halpern PhD student Google Brain
Nathan Silberman PhD student Butterfly Network
Issac Lage Research engineer PhD Student, Harvard
Jake Marcus Research engineer Google Brain
Arjun Khandelwal Master's student TGS Management
Helen Zhou Master's student PhD student, CMU
Hunter Lang Master's student Microsoft Research
Aahlad Manas Master's student PhD student, NYU
Ankit Vani Master's student PhD student, MILA
Cafer Yildirim Master's student Finance
Chill Yi-I Chiu Master's student Google
Eliot Brenner Master's student
Jingshu Liu Master's student
Justin Mao-Jones Master's student Apple
Maya Rotmensch Master's student Facebook
Yoon Kim Master's student PhD student, Harvard
Shannon Hwang Undergrad
David Amirault Undergrad
Alok Puranik Undergrad
Suchan Vivatsethachai Undergrad
Rares Buhai Undergrad
Loc Trinh Undergrad
Aarti Bagul Undergrad Master's student, Stanford
Dmitriy Afanasyev Undergrad LinkedIn
Do Kook Choe Undergrad PhD student, Brown
Yitao Li Undergrad Facebook


Steven Horng, MD (Harvard Medical School / Beth Israel Deaconess Medical Center)
Saul Blecker, MD, MHS (NYU Langone, Division of Healthcare Delivery Science)
Leora Horwitz, MD, MHS (NYU Langone, Director of Center for Healthcare Innovation and Delivery Science)
Tassilo Klein, PhD (SAP Innovation Center)
Independence Blue Cross
Memorial Sloan Kettering Cancer Center
Observational Health Data Sciences and Informatics (OHDSI)