Prof. Collin M Stultz

Nina T and Robert H Rubin Professor in Medical Engineering and Science
Director, Harvard-MIT Health Sciences and Technology Program (HST)
Associate Director, Institute for Medical Engineering and Science (IMES)

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 36-796

Assistant

Megumi Masuda-Loos
megumima@mit.edu

Areas of Interest and Expertise

Machine Learning for Healthcare
Cardiovascular Risk Stratification
Explainable Machine Learning Models
Molecular Simulations
Protein Structure and Dynamics
Biophysics
Disease Models
Atherosclerosis
Alzheimer's Disease
Rheumatoid Arthritis
Stochastic Models
Function Optimization
Conformational Changes in Macromolecules and the Effect of Structural Transitions on Human Diseases\nGain Insights into the Role of Molecular Structure by Utilizing Techniques Drawn from Computational Chemistry, Signal Processing, and Basic Biochemistry

Research Summary

Currently research in Professor Stultz's group is focused on the development of machine learning tools that can guide clinical decision making.

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.

Recent Work

  • Video

    Collin Stultz - 2016-Digital-Health_Conf-videos

    September 14, 2016Conference Video Duration: 37:55

    Computational Biomarkers for Assessing the Risk of Death after a Heart Attack

    Cardiovascular disease remains the leading cause of death in the industrialized world. Although research into the etiology and treatment of cardiac disease remains a focus of numerous research groups, the accurate identification of patients who are at risk of adverse events following a heart attack remains a major challenge in clinical cardiology. In this talk I will describe how sophisticated computational biomarkers, which integrate a diverse array of clinical information, can be used to identify patients who are at elevated risk of death after a cardiac event. This work demonstrates that computational biomarkers can provide useful and powerful insights that can help guide clinical decision making.

    2016 MIT Digital Health Conference