My research interests involve using computational methods from dynamical systems theory, complexity science, and machine learning to explain new physiological phenomena, advance biomedicine, and provide predictive capabilities that can change human health through better clinical decision-making. I am particularly interested in all things related to vasculature.
Complexity theory involves the study of systems whose components interact non-linearly and at many different scales. Nearly all physiological systems can be conceptualized through a complex systems framework. Interactions between trillions of heterogeneous signaling molecules and cells form sophisticated pathways and feedback loops, allowing humans to conquer incredible physical and intellectual feats; from flying to the moon or summiting the tallest mountain on earth to the surprisingly intricate tasks of breathing, blinking, thinking, and even simply being. These complex physiological processes are dynamic - changing over time - and sometimes becoming dysfunctional. In some sense, this dysfunctionality could be used to explain every death or disease in history. In this framework, the goal of medicine is to identify where the system has become dysfunctional and fix the problem. Of course, this is nothing close to a trivial question, and some types of dysfunction are more accessible to identify and repair than others. A central challenge is that in the medical realm, clinically accessible data are never a direct measurement of these fundamental driving processes. Instead, they reflect the collective dynamics of these many processes riddled with noise and non-stationarity effects. Therefore, my research is focused on how we can use tools from complex systems theory to map this observable behavior to information about the function (or dysfunction) of the many physiologic processes forming that behavior and identifying the pathologies or functionality associated with those fundamental physiologic processes.