Yaozhong Shi

I am a final-year PhD candidate in Mechanical Engineering at Caltech, with a minor in Applied and Computational Mathematics. I have the privilege of being advised by Domniki Asimaki, and co-advised by Zachary E. Ross and Kamyar Azizzadenesheli.

My research develops and applies novel machine-learning techniques to solve complex scientific problems. I focus on operator learning theory, generative models in function space (especially flow matching), Bayesian methods, and multimodal large language models for large-scale PDE solving. I apply these methods to critical challenges in earthquake engineering and related areas of scientific machine learning.

My long-term goal is to build AI systems that unify deep understanding of the physical world with sophisticated reasoning and adaptive behavior—systems that enable fundamental scientific breakthroughs and power real-world applications. This vision bridges foundational research in operator learning, generative AI, multimodal LLMs, computer vision, and reinforcement learning, and translates it into the deployment of robust, intelligent agents capable of operating in complex, dynamic environments.