About Me

As a research assistant in Industrial Engineering at Texas A&M University, I am passionate about applying intelligent and rational decision-making methods to various domains. My research focuses on inverse reinforcement learning and game theory, especially the partially observable stochastic game, which models the multi-agent interaction with imperfect information.

I have applied my model to several industries: fast food, autonomous vehicles, healthcare, and baseball games. For example, using Markov decision processes and hidden Markov models, I developed better strategies for opening or closing stores for five burger companies under different business cycles in the fast food industry. I have also leveraged machine learning and deep learning techniques to solve classification and recognition problems, such as fault diagnosis for 3D printers and emotion recognition for human movements. I have a solid mathematical background, with a BA in Mathematics from Beihang University, where I received multiple scholarships and honors. Currently, I am seeking applications in the healthcare domain that will allow me to explore further and contribute to the field of intelligent systems.