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)
Associate Director, Institute for Medical Engineering and Science (IMES)
Primary DLC
Department of Electrical Engineering and Computer Science
MIT Room:
36-796
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
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.
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.
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Projects
December 10, 2020Department of Electrical Engineering and Computer Science
Risk Stratification for Patients with Cardiovascular Disease
Principal Investigator Collin Stultz
December 1, 2020Department of Electrical Engineering and Computer ScienceComputational Cardiovascular Research Group
Principal Investigator Collin Stultz
November 10, 2020Department of Electrical Engineering and Computer ScienceModeling the Unfolded State of Disordered Proteins
Principal Investigator Collin Stultz
Cardiovascular Data Science for Personalized Medicine
Principal Investigator Collin Stultz
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Video
Collin Stultz - 2016-Digital-Health_Conf-videos
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.