Computational Cardiovascular Research Group

Research in the RLE 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.

Research in Professor 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.  His research group employs an interdisciplinary approach that utilizes techniques drawn from computational chemistry, signal processing, and basic biochemistry.