|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.
Modeling the Unfolded State of Disordered Proteins
Risk Stratification for Patients with Cardiovascular Disease
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.