My interest in improving medical care developed at an early age – both of my parents work in healthcare, and their
daily routines were a common theme over dinner. In keeping with my healthcare interest, I decided to study biomedical engineering at UT Austin, where I quickly discovered a passion for the computational tools used in scientific research. After this discovery, I decided to focus on computational approaches within biomedical engineering. During the second half of my degree, I worked under Dr. Michael Sacks at UT Austin, where I assisted graduate students in building computational models of the mitral valve. Meanwhile, I completed minors in both computer science and computational engineering. In August 2021, I began my PhD at The University of Michigan, where I’ve been focusing on super-resolution 4D flow MRI and cardiovascular relative pressure estimations.
I’m passionate about combining theory-driven and data-driven approaches to modeling the cardiovascular system. My experience in computer science classes gave me insight into the tremendous possibilities of machine learning, and my experience in engineering classes taught me the value in bounding simulations with actual physics. I’m particularly interested in physics-informed neural networks and similar tools at the moment. Ultimately, I’d like to develop computational tools that help clinicians make quantitatively-driven decisions about patient care.