Collin M. Stultz
Professor, Electrical Engineering and Computer Science (EECS); Professor, Institute of Medical Engineering and Sciences (IMES)
Collin Stultz is a professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Harvard-MIT Division of Health Sciences and Technology, Institute of Medical Engineering and Sciences, and Research Laboratory of Electronics. He is also a cardiologist at the Massachusetts General Hospital. His Computational Cardiovascular Research Group is focused on developing and applying machine learning models that identify patients at high risk of adverse clinical events, and that identify optimal treatment strategies for high risk patients. His group combines computational modeling and machine learning to accomplish these tasks.
Stultz has received a Burroughs Wellcome Fund Career Award and an NSF Career Award. He is a member of the American Society for Biochemistry and Molecular Biology, the Federation of American Societies for Experimental Biology, and the American Institute for Medical and Biological Engineering College of Fellows. He earned a PhD in biophysics at Harvard University, an MD from Harvard Medical School, and completed his clinical training in internal medicine and cardiovascular disease at Brigham and Women’s Hospital.
- Dai, W., Ng, K., Severson, K., Huang, W., Anderson, F., Stultz, C. M. (2019). Generative Oversampling with a Contrastive Variational Autoencoder. IEEE International Conference on Data Mining (ICDM), 101-109.
- Myers, P. D., Huang, W., Anderson, F., Stultz, C. M. (2019). Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome. Nature Scientific Reports 9 (1), 1-9.
- Burger, V. M., Vandervelde, A., Hendrix, J., Konijnenberg, A., Sobott, F., Lorisand, R., Stultz, C. M. (2017). Hidden States with Disordered Regions of the CcdA Antitoxin Protein. Journal of the American Chemical Society, 139 (7): 2693-2701.
- Liu, Y., Syed, Z., Scirica, B. M., Morrow, D.A., Guttag, J. V., Stultz, C. M. (2014). ECG morphological variability in beat space for risk stratification after acute coronary syndrome. Journal of the American Heart Association 3 (3), e000981.
- January 23, 2020: MIT News, Technique reveals whether models of patient risk are accurate.
- September 21, 2014: MIT News, Engineered proteins stick like glue — even in water.