About Collin Stultz, MD, PhD

Dr. Collin M. Stultz received his AB from Harvard College in Mathematics and Philosophy in 1988 and his MD from Harvard Medical School and a PhD in Biophysics from Harvard University in 1997.  He completed his internship, residency and cardiology fellowship at the Brigham and Women's Hospital.  He is a board certified cardiologist and a tenured Professor at the Massachusetts Insttiute of Technology.  Dr. Stultz is on the faculty of both the Harvard-MIT Division of Health Sciences of Technology (HST), the Institute of Medical Engineering and Science (IMES), and MIT’s Department of Electrical Engineering and Computer Science. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology. Among his honors are being a recipient of the Burroughs Wellcome Fund Career Award in Biomedical Sciences and the James Tolbert Shipley Prize.  His clinical interests include the diagnosis and treatment of patients with all forms of cardiovascular disease.

Departments, Centers, & Programs:

Clinical Interests:



Mass General Heart Center
55 Fruit St.
Boston, MA 02114
Phone: 866-644-8910

Medical Education

  • PhD, Harvard Graduate School of Arts and Sciences
  • MD, Harvard Medical School (Massachusetts)
  • Residency, Brigham and Women's Hospital
  • Fellowship, Brigham and Women's Hospital

American Board Certifications

  • Cardiovascular Disease, American Board of Internal Medicine

Accepted Insurance Plans

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Research in Dr. Stultz’ group revolves around two general themes.  Firstly, a major thrust of the group is to use computational methods to understand conformational changes in macromolecules and the effect of structural transitions on common human diseases. Secondly, his group draws upon concepts in signal processing and machine learning to develop computational biomarkers that identify patients at high risk of adverse cardiovascular events. 

We have developing novel methods to model the unfolded states of intrinsically disordered systems.  This exercise is of paramount importance as many disordered proteins have been implicated in a number of neurodegenerative disorders, such as Alzheimer's disease.. We have applied our methods to understand the unfolded state of the intrinsically disordered proteins, tau protein, alpha synuclein, and abeta. Our goal is to understand how these proteins contribute to the pathogenesis of disease using a combination of physically based calculations and biochemical data. 

We are interested in developing automated methods that can identify patients with cardiovascular disease who are at high risk of adverse outcomes.  To do this we employ a variety of different methods grounded in signal progressing and machine learning. Our methods combine disparate types of clinical information (e.g., medical history, genetic information, physiologic signals) to arrive at models that can guide clinical decision making.


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