Collin M. Stultz
Nina T. and Robert H. Rubin Professor in Medical Engineering and Science, Department of Electrical Engineering and Computer Science; Associate Director, Institute of Medical Engineering and Sciences ; Co-director, Harvard-MIT Division of Health Sciences and Technology
Who they work with
Categories
Collin Stultz is the Nina T. and Robert H. Rubin Professor in Medical Engineering and Science in MIT’s Department of Electrical Engineering and Computer Science (EECS), co-director of the Harvard-MIT Division of Health Sciences and Technology (HST), associate director of the Institute of Medical Engineering and Sciences (IMES), an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and a member of the Research Laboratory of Electronics (RLE) at MIT. He is also a practicing 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.
Selected Publications
- Ng, K., Severson, K., Stultz, C., Dai, W., Myers, P., Kartoun, U., Huang, W., Anderson, F. (2020). Identifying Unreliable Predictions in Clinical Risk Models. Nature Digital Medicine.
- 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.
Media
- 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.