I am a Ph.D. student in computer science at the Kahlert School of Computing, University of Utah. I earned my Bachelor of Science degree from the University of Arizona in computer science and mathematics.

I am on the job market for a full-time role starting in summer/fall 2026. I previously interned at Meta (2024 summer) and DoorDash (2025 summer) working on developing generative recommendation models (Meta) and homepage ranking models (DoorDash). I am interested in machine learning engineer and machine learning applied scientist roles. Please refer to my resume for more details.

My PhD research focuses on developing surrogate-based AI models that learn to emulate and reason about complex processes, including both physical systems and human expertise:

• Surrogates for human experts: I align large language models to act as intelligent surrogates for personalized healthcare coaching and recommendations, using customized techniques such as supervised fine-tuning, reinforcement learning, and chain-of-thought reasoning.
• Surrogates for physical dynamical systems: I develop probabilistic and generative surrogates for complex physical dynamics such as climate evolution, plasma dynamics, and turbulent fluid flows, to enable efficient simulation, forecasting, and uncertainty quantification.

I am advised by Dr. Shandian Zhe.

My research interest includes: Surrogate Modeling, LLM Alignment, Reinforcement Learning