Natalie Simonian’s Mission to Simulate the Human Heart

When Natalie Simonian stepped into the catheterization laboratory for the first time dressed in scrubs and standing just inches from a patient undergoing heart valve repair, something clicked. This wasn’t just data anymore. It was personal.

Simonian, who recently received her Ph.D. in Biomedical Engineering at The University of Texas at Austin, works in the Willerson Center for Cardiovascular Modeling and Simulation at the Oden Institute for Computational Engineering and Sciences. It is here where she combines patient imaging data with advanced computational tools to simulate and optimize mitral valve repair. Her work lies at the heart of computational medicine, a rapidly growing field that combines engineering, data science, and clinical knowledge to advance healthcare.

“Natalie has done a phenomenal job in developing the first patient-specific mitral valve computational model capable of predicting surgical repair of the mitral valve, based solely on pre-operative clinical imaging data,” said her advisor, Professor Michael Sacks, Director of The Willerson Center. “This will allow for true pre-operative planning, as well as the development of mitral valve digital twins for follow-up data integration to improve long-term analysis.”

This fusion of clinical insight and computational precision is what drives Simonian’s work. “Sometimes in the lab, you just see patients as data points or image sets. But when you’re in the hospital, right there watching a surgeon troubleshoot a difficult case, you remember why this work matters,” she said. “You get emotionally invested.”

Her research focuses on transcatheter edge-to-edge repair, a minimally invasive technique used to treat mitral regurgitation, which affects millions globally. But outcomes vary, and predicting which patients will benefit remains a challenge. Simonian’s computational models, built on real patient imaging, aim to provide surgeons with a clear window into the future, before the operation begins.

Simonian’s path to computational medicine wasn’t linear. As an undergraduate at The University of California, Berkeley, she studied bioengineering with a minor in Japanese language and culture. She describes herself as curious and self-directed. “I like going down rabbit holes,” she said with a laugh. “But what I’ve learned during my Ph.D. is how to balance that curiosity with productivity, and to always return to the bigger picture.”

A significant part of that growth came from her participation in the Oden Institute's Computational Medicine Portfolio, an interdisciplinary certificate program. In fulfilling the program’s biological and clinical science requirement, Simonian enrolled in a graduate-level pathophysiology course at  UT’s School of Nursing. “It was eye-opening,” she said. “The nurses were learning to solve problems in a totally different way. I’d ask about mechanisms, and they’d say, ‘We care if the patient is feeling better.’ Both mindsets are valid and necessary, and learning to communicate across them is essential if you want your engineering work to be useful in the clinic.”

This cross-disciplinary fluency has helped shape Simonian’s research philosophy: rigorous yet grounded. “Growing up in Silicon Valley, I saw a lot of doctors design things that wouldn’t work and engineers design things no one needed,” she said. “This experience helped me see how important it is to keep the end user [clinicians and patients] at the center of the design process.”

She credits Sacks for encouraging her independence while guiding her focus. “He’s been incredibly supportive. He encouraged us to give our own talks at conferences, which has taught me how to communicate research to all kinds of audiences,” she said. With his support, Simonian has attended six to seven conferences a year, often presenting her work to both engineering experts and clinicians.

For someone who’s already shaping the future of heart repair, Simonian remains remarkably grounded. She’s fluent in five languages, has a passion for travel and drawing, and values time spent with friends as much as time spent debugging simulations.

Looking ahead, Simonian is optimistic about the future of computational medicine. “The applications are huge,” she said. “Every time I’m at a clinical meeting and someone says, ‘If only we could predict this or simulate that,’ I think—this is what I’m working on.”