- Combined machine learning (ML) and flow-based reduced-order models (ROMs) to identify flow metrics that can differentiate between cardiovascular phenotypes and can be utilized for classification. - Achieved classification performance measures (area under ROC curve) of 72-92% differentiating doppler ultrasound left ventricular flow of healthy, dilated cardiomyopathy, and hypertrophic cardiomyopathy patients.

- Trained clinicians on protocol for multicenter study, promoted site engagement to improve product usability, gathered user feedback, and developed a data collection checklist assisting sonographers during surgery to reduce user-risk in specific environments.- Managed Veocor’s platform, oversaw accounts for over 100 physicians, and provided feedback on data uploads. - Generated patient-specific intraventricular fluid models to non-invasively assess blood stasis and thrombosis risk in patients with left ventricular assist devices (LVAD).- Developed an analytical tool for hemodynamic monitoring using data driven methods, expanding the number of analyzable cases from 21 to 98 and providing a novel engineering solution for our collaborators at Medtronic.

Graduate courses: MAE 209A: Efficient Numerical Methods for Simulations, Optimization, and Controls (Fall 2017) MDE 290: Mechanical Transports for Biomedical Devices (Winter 2017, 2018)Undergraduate courses: MAE 101A: Fluid Mechanics (Winter 2019) MAE 107: Computational Methods for Engineers (Summer 2018) MAE 180A: Spacecraft Guidance (Fall 2018), MAE 170: Experimental Techniques for Engineers (Spring 2017, 2018, 2019)