I’m a Visiting Researcher with Google's Verily and a post-doc with Dr. Peter Szolovits in MEDG (CV); in Fall 2018 I will be joining the University of Toronto as an Assistant Professor in Computer Science and Medicine, affiliated with the Vector Institute. I have worked with Dr. John Guttag at CSAIL, and have clinical collaborations at Beth Israel Deaconess Medical Center and Massachusetts General Hospital.

I've organized the NIPS 2014 Women in Machine Learning Workshop, the NIPS 2016 Workshop on Machine Learning for Health, the 2017 Workshop on Machine Learning for Health and MIT's first Hacking Discrimination event.

I’m interested in creating and applying machine learning algorithms towards improved prediction and stratification of relevant human risks; you can find my thesis defense talk here.

My research focuses on:

Good Representations for Post-Discharge Outcome Prediction

Ghassemi, M., Naumann, Doshi, Brimmer, Joshi, Rumshisky, and Szolovits. (KDD 2014);
Ghassemi, M.*, Pimentel*, Naumann, Brennan, Clifton, Szolovits, and Feng. (AAAI 2015);
Rumshisky, Ghassemi, M., Naumann, Szolovits, Castro, McCoy, and Perlis. (Translational Psychiatry – Nature 2016)

Early Prediction of Actionable In-Patient ICU Interventions

Ghassemi, M.*, Wu*, Feng, Celi, Szolovits, and Doshi-Velez. (JAMIA 2016);
Ghassemi, M*, Wu*, Hughes*, Szolovits, and Doshi-Velez. (AMIA-CRI 2017);
Suresh, Hunt, Johnson, Celi, Szolovits and Ghassemi. (MLHC 2017);
Raghu, Komorowski, Celi, Szolovits, and Ghassemi. (MLHC 2017);
McDermott, Yan, Naumann, Hunt, Suresh, Szolovits, and Ghassemi. (AAAI 2018)

Evidence-based Diagnosis and Feedback in Wearable Outpatient Devices

Ghassemi, M., Van Stan, Mehta, Zañartu, Cheyne, Hillman, and Guttag. (IEEE TBME 2014);
Ghassemi, M., Syed, Mehta, Van Stan, Hillman, and Guttag. (MLHC 2016/JMLR W&C V56)