Faculty Frontiers Seminar

Faculty Frontiers is a seminar series that invites professors to present their research areas to students. It offers an engaging platform for students to explore diverse academic fields, learn about cutting-edge research, and interact with faculty members. The series aims to inspire curiosity, foster interdisciplinary connections, and enhance students’ understanding of various disciplines. This will be a monthly seminar.

Date and Time (China standard time): Friday, Sep 12, 10:30 am – 11:30 am

Location: WDR 1007

Zoom: 610 581 1266

Title: Mechanical-driven and Data-driven Modeling and Applications

Speaker: Dr. Shixin Xu (Duke Kunshan University)

Abstract: 

In this talk, I will share my research on mathematical modeling from two complementary perspectives.

The first is mechanics-driven modeling, where I will present a multiscale framework for modeling biological interfaces. This includes the development of models that couple physical laws—such as fluid dynamics, ion transport, and membrane mechanics—to understand complex biological processes at different spatial and temporal scales.

The second perspective is data-driven modeling, where I will showcase our recent work on disease management and predictive modeling. This includes collaborations with healthcare and industrial partners, where we integrate domain knowledge and data analytics to guide decision-making and optimize real-world systems.

Together, these two approaches illustrate how mathematical modeling can serve as a powerful bridge between theoretical insight and practical application.

Bio: 

Dr. Shixin Xu’s research focuses on integrating mathematics, life sciences and data sciences to solve challenging real-world problems such as investigating underlying mechanisms leading to cardiovascular and cerebrovascular diseases. The first theme of his research is modeling complex fluids systems in biological systems like central nerve system and muscles. The second thrust of his research is developing efficient and structure-preserving numerical schemes for solving these model systems. The third theme of his research is related to machine learning. Data-driven models are proposed to predict the risks of chronic diseases, like Diabetic kidney disease. Physics-informed coupling data-driven models are used to predict health status of computer chips and DRAM.

Throughout his career, Prof. Xu has supervised undergraduate projects in several directions, including dynamical systems (Neuron-glia-extracellular coupling system), mathematical model (epidemic disease and noninvasive room occupation detection), machine learning (Stroke risk factor identification and Bitcoin price prediction) and others. He is interested in mentoring Signature Work projects in topics related to his research interests in mathematical modeling and data-driven model in biological and clinical problems, like debates and strokes.