Exploring the Frontiers of Computational Medicine

ABSTRACT
Computational models have greatly improved how we understand complex biological systems. Yet, the variety of these systems prohibits a one-size-fits-all solution. Hence, to effectively tackle the specific challenges posed by varying contexts within computational medicine, we must tailor our computational strategies whether they be data-driven, knowledge-driven, or a hybrid approach integrating the two.
In this talk, I will dissect the unique strengths and situational superiority of each modeling paradigm in computational medicine. First, I will show how to provide accurate predictions and distill novel biological knowledge using data-driven models. Next, I will demonstrate how to validate observed disease-mediated changes in blood rheology via knowledge-driven models. Additionally, I will also discuss patient-specific decision-making enabled by a hybrid model.
I will conclude this discussion by focusing on the crucial factors, such as age and sex, that are essential to tailoring treatments in precision medicine, and how to synergistically integrate data-driven, knowledge-driven, and hybrid models to tackle these challenges.

BIOGRAPHY
Yixiang Deng is currently a Postdoctoral Fellow at the Ragon Institute of Mass General, MIT, and Harvard with a joint appointment at the MIT Department of Biological Engineering, advised by Prof. Douglas A. Lauffenburger. Her research focuses on integrating knowledge-driven and data-driven computational models to probe the mechanisms of diseases and the effectiveness of therapeutics. Before joining the Ragon Institute, she completed her Ph.D. in the School of Engineering advised by Prof. George Em Karniadakis at Brown University in 2021. She received a B.Eng. degree in Engineering Mechanics at Shanghai Jiao Tong University in 2015. She is the recipient of multiple honors and awards, including the Brown
University Open Graduate Education Fellowship in 2018, MIT Rising Star in Mechanical Engineering in 2021, the Mark and Lisa Schwartz AI/ML/ Immunology Initiative Fellowship in 2022, and the Duke Engineering Future Faculty of Innovation and Excellence (DEFINE) program in 2023.