In 2008, I recieved my BSc from New Mexico State University where I studied Electrical Engineering, Applied Mathematics and Computer Science. My undergraduate thesis was a historical analysis of first century Christian literature. In 2011 I recieved an MPhil in Information Engineering from the University of Cambridge (UK). My master's thesis demonstrated how to extract complex mental representations using simple tasks. In 2018, I completed a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. My Ph.D. thesis (slides) describes several machine learning methods for the prognostication of coma, after cardiac arrest.

Selected Distinctions


As an undergraduate, I recieved the Goldwater Scholarhship, and graduated as my school's Outstanding Engineer. I was the recipient of the Gates-Cambrdige scholarship, which generously funded my MPhil. As a doctoral student, I have been supported by competitive grants from the Salenrno Foundation, and the National Institutes of Health.

Invited Talks

I am honored to have been invited to speak about my work with others in academia and industy. In 2017, I delivered a keynote address at ELM 2017, a symposium at ACNS and presented at Samsung's innovation headquarters. In 2018, I delivered Invited talks at Boston's Medical Development Group, ACM's Conference on Reccomendation Systems, and the Intelligent Health conference. For a full list of invited talks, see my CV.

Knowledge Sharing


Knowledge should be freely shared. I am a certified teacher that has organized several hackathons, contributed to reviews in my field, and helped teach courses on Machine Learning (HST.953 workshop, IAP.ML4CC slides + code). I was also an editor and author for a book on the secondary analysis of electronic health records.

Suggested and Potential Readings

Thinking as a Science by Haslett
It was published over 100 years ago, but it's some of the best advise I've ever read.

Automated Machine Learning by Hutter, Kotthoff and Vanschoren
Hyper-parameter optimization is a pain point for machine learning practitioners; this book describes ways to automate that process.

Copyright, Mohammad M. Ghassemi, 2019