Marzyeh Ghassemi


Affiliate Scientist, LKS-CHART; Assistant Professor, Computer Science and Medicine, affiliated with the Vector Institute; Visiting Researcher with Google’s Verily

The coolest things I've done in my career are:

  • This year I have been honored to be receive an MIT Tech review 35 Innovators Under 35 Award, an NSERC 2018 Discovery Grant, and MIT's Seth J. Teller Award for Excellence, Inclusion and Diversity.

  • 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.

  • This year I am a Guest Editor on the 2018 PLoS ONE call on Machine Learning in Health and Biomedicine, speaker at The Digital Doctor: Health Care in an Age of AI and Big Data IACS SYMPOSIUM, a panelist at AMIA 2018 Informatics Summit Panel on Deep Learning for Healthcare - Hype or the Real Thing?

If I could have a super power it would be:

Time travel, because then we could generate counter-factuals.

I'm a "closet" fan of:


The nerdiest thing I do in my spare time is:

Solder electronics.

Three things still on my bucket list are:

  1. Solve healthcare inequity

  2. Fly a hot air balloon

  3. Visit Machu Piccu

My past experience includes:

I'm a Visiting Researcher with Google’s Verily and a post-doc in the Clinical Decision Making Group at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Dr. Peter Szolovits. I'm joining  the University of Toronto as an Assistant Professor in Computer Science and Medicine in Fall 2018, and will be affiliated with the Vector Institute.

My research focuses on machine learning with clinical data to predict and stratify relevant human risks, encompassing unsupervised learning, supervised learning, structured prediction. My work has been applied to estimating the physiological state of patients during critical illnesses, modelling the need for a clinical intervention, and diagnosing phonotraumatic voice disorders from wearable sensor data.

My work has appeared in KDD, AAAI, IEEE TBME, MLHC, JAMIA, and AMIA-CRI; she has also co-organized the NIPS 2016 Machine Learning for Healthcare (ML4HC) and 2014 Women in Machine Learning (WIML) workshops. Prior to MIT, I received B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University, worked at Intel Corporation, and received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar.

My recent insights and projects include: