School of Medicine


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  • Vedant Pargaonkar

    Vedant Pargaonkar

    Member (Staff), Cardiovascular Institute

    BioMy long-term research interests involve development of algorithms using computational methods for early detection of coronary pathophysiology including, endothelial dysfunction and microvascular dysfunction (MVD) and/or a myocardial bridge (MB) in patients with angina and no obstructive coronary artery disease (NOCAD) and the identification of novel target therapies for primary prevention and improved prognosis in these patients. Under the mentorship of Dr. Jennifer Tremmel in Cardiovascular medicine at Stanford, I have been systematically studying to better understand the underlying pathophysiology of these patients, as well as the optimal use of diagnostic testing and treatment using the angina and no-obstructive CAD Registry at Stanford. In collaboration with other investigators in this field, we have published multiple scientific articles highlighting the limitations of current testing in this population and identification of novel diagnostic tools for early diagnosis and management of patients with angina and no obstructive CAD. My research also focuses on myocardial infarction (MI) in women, particularly spontaneous coronary artery dissection (SCAD). I have been involved in the design and execution of the first international collaborative study in SCAD, investigating peripartum vs. non-peripartum SCAD. This is analyzing the largest cohort of patients recruited from multiple US and non-US sites to understand the pathophysiological differences in these patient cohorts.

  • Martin Pfaller

    Martin Pfaller

    Instructor, Pediatrics - Cardiology

    BioDr. Martin R. Pfaller is an Instructor in the Department of Pediatrics (Cardiology) in the group of Alison L. Marsden. He received his B.Sc., M.Sc., and Ph.D. in Mechanical Engineering from the Technical University of Munich, working with Wolfgang A. Wall. During his Ph.D., he validated an efficient yet physiologically accurate boundary condition to account for the mechanical support of the heart within its surroundings, which has been adopted by various research groups worldwide. He further demonstrated how projection-based model order reduction could speed up model personalization from patient data, such as magnetic resonance imaging or blood pressure measurements. His current work focuses on cardiovascular fluid dynamics. He developed reduced-physics models to make blood flow simulations faster and more reliable. Further, he implemented a fluid-solid-growth interaction model in blood vessels in collaboration with Jay D. Humphrey at Yale University. His future research will predict the heart’s long-term function in heart diseases, supported by an NIH Pathway to Independence Award (K99/R00). He will quantify the risk of heart failure after a heart attack with a stability analysis validated with imaging data in swine and humans. This research will improve our understanding of biomechanical mechanisms leading to heart failure and help to identify patients at risk, enable personalized therapies, and facilitate the optimal design of medical devices.