Xun Tang

Control theory studies the design strategies to deliver desired performance of the process of interest. Applications of control theory has benefited a wide range of fields from self-assembly to gene expression regulation.

In this talk, I will focus on a colloidal self-assembly system and a wound healing process to discuss the application of mathematical modeling, including both data-driven and first principle-based approaches and control theory, in tackling challenging issues with colloidal self-assembly and the regulation of biological processes. Specifically, I will talk about the current efforts from the group on developing a generalizable optimal control framework for the control of a high dimensional stochastic colloidal self-assembly process, especially on state representation and control inputs identification. I will also talk about the ongoing work on how we leverage model-based analysis and synthetic gene circuits to realize regulation of wound healing process. 

Dr. Xun Tang obtained his B.S. in Chemical Engineering from University at Buffalo in 2011, and Ph.D. in Chemical Engineering from Georgia Tech in 2016, with his thesis work focused on optimal control for colloidal self-assembly. He then worked at postdoc at UC Riverside and Penn State University, before he joined Ford as a research engineer in 2018.

Since 2020, Dr. Xun Tang joined the Cain Department of Chemical Engineering at Louisiana State University as a tenure track Assistant Professor. The current focus of research in his lab is on machine learning, optimal control, molecular self-assembly, and synthetic biology. 

• Time: 11:00 AM
• Location: 206 Furnas Hall