- Professor of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science
Dr. Collin M. Stultz is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), a faculty member in the Harvard-MIT Division of
Health Sciences and Technology, a Professor in the Institute of Medical Engineering and Sciences at MIT, a member of the Research Laboratory of Electronics (RLE), and an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a practicing cardiologist at the Massachusetts General Hospital (MGH). Dr. Stultz received his undergraduate degree in Mathematics and Philosophy from Harvard University; a PhD in Biophysics from Harvard University; and a MD from Harvard Medical School. He did his internship, residency, and fellowship at the Brigham and Women’s Hospital in Boston. His scientific contributions have spanned multiple fields including computational chemistry, biophysics, and machine learning for cardiovascular risk stratification. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology and he is a past recipient of a National Science Foundation CAREER Award and a Burroughs Wellcome Fund Career Award in the Biomedical Sciences. Currently, research in his group is focused on the development of machine
learning tools that can guide clinical decision making.
- PhD in Biophysics, Harvard University, 1997
- MD, Harvard Medical School, 1997
- AB, Harvard College, 1988
- Burroughs Wellcome Fund Career Award in Biomedical Sciences
- James Tolbert Shipley Prize
- American Society for Biochemistry and Molecular Biology
- Federation of American Societies for Experimental Biology
- American Chemical Society
Research in the Computational Cardiovascular Research Group is focused on three areas: 1) Understanding conformational changes in biomolecules that play an important role in common human diseases, 2) Using machine learning to develop models that identify patients at high risk of adverse clinical events, and 3) Developing new methods to discover optimal treatment strategies for high risk patients. The group uses an interdisciplinary approach combining computational modeling and machine learning to accomplish these tasks.
- Myers PD., Scirica BM., Stultz Cm., Machine Learning Improves Risk Stratification After Acute Coronary Syndrome. Nature Scientific Reports 7, 12692, (2017)
- Burger VM., Vandervelde A., Hendrix J., Konijnenberg A., Sobott F., Lorisand R., Stultz CM., Hidden States with Disordered Regions of the CcdA Antitoxin Protein. Journal of the American Chemical Society, 139 (7): 2693-2701, (2017)
- Yun L., Syed Z., Scirica BM, Morrow DA, Guttag JV, Stultz CM. ECG Morphological Variability in Beat-space for Risk Stratification after Acute Coronary Syndrome. Journal of the American Heart Association, 2014;3:e000981 doi:10.1161/JAHA.114.000981
- Fisher CK., Huang A., Stultz CM. Modeling Intrinsically Disordered Proteins with Bayesian Statistics. Journal of the American Chemical Society 132, 14919-14927, (2010)
A full list of Dr. Stultz’s publications can be found on his website.
- 6.03 – FA 2013 – Introduction to EECS II from a Medical Technology Perspective