PICS Colloquium with Xianqiao Wang: Decoding Living Systems with AI and Computational Models: From Brain Development to Nano–Bio Interfaces

Abstract:

Biomechanics must confront the inherent complexity of living systems and the limited ability of experiments alone to capture phenomena across scales. AI- and data-empowered computational mechanics offer powerful ways to bridge gaps in spatial and temporal resolution, generate predictive insight, and guide biomaterial design, disease diagnosis, and therapeutic strategies. This seminar illustrates how computational modeling can reveal the mechanics underlying complex biological processes through two current studies. The first examines brain development, asking how cortical folding and neural connectivity interact during neurodevelopment. By combining neuroimaging with computational biomechanics, we investigate how differential growth and axonal wiring produce both the variability and regularity of cortical folds, providing mechanistic insight into brain structure–function relationships and neurodevelopmental disorders. The second study explores nano–bio interface mechanics and how nanoparticle properties—including surface chemistry, shape, and stiffness—control cellular uptake and release. Multiscale simulations reveal design principles for tuning nanomaterial–tissue interfaces while preserving structural integrity. Together, these examples show how mechanics, data, and AI can transform multiscale observations into actionable design rules. The seminar concludes with a forward-looking vision for integrating AI and data-driven computational mechanics to advance biomechanics research.

Bio: 

Dr. Xianqiao (XQ) Wang is a Professor of Mechanical Engineering at the University of Georgia, where he also serves as Director of Graduate Studies for the Engineering program and Co-Director of the UGA Center for Brain-Inspired Artificial Intelligence. He earned his Ph.D. in Mechanical Engineering from George Washington University in 2011 and subsequently joined the University of Georgia as an Assistant Professor in 2012. Dr. Wang has published more than 150 peer-reviewed articles in leading journals, including Nature Communications, Science Advances, Advanced Materials, ACS Nano, Advanced Functional Materials, Brain Structure and Function, Cerebral Cortex, and Human Brain Mapping, etc. His research spans data-driven brain mechanics, bio-inorganic interfaces, ML/AI-enabled materials design, and soft matters. His current research is supported by NSF, NIH, USDA, DOE, and IES grants.