Hongjia (Alex) Huang

My name is 黄泓嘉 (Hongjia Huang), also known as Alex. I am a junior at New York University Shanghai, double majoring in Computer Science and Mathematics (GPA 3.94/4.0). I am broadly interested in building AI systems that are physically grounded — models that understand, simulate, and reason about the world as it actually works, from molecular dynamics to robot manipulation.

My research sits at the intersection of physics-informed machine learning, generative models, computer vision, and embodied AI. I am particularly drawn to questions about how physical constraints — ranging from atomic-scale interactions to macroscopic motion — can serve as inductive biases for learning representations that are stable, interpretable, and generalizable.

Previously, I worked with Professor Shengjie Wang at NYU Shanghai on efficient training of graph neural networks for molecular force field prediction, using sub-modular data selection and geometric-aware latent representations. More recently, I joined Professor Tianyi Zhou and Professor Furong Huang at UMD for a summer research internship, where I worked on physics-informed vision-language-action (VLA) models — designing trajectory prediction modules, integrating 3D visual features, and developing diffusion-based future trace prediction for robotic planning.

My interest in physics traces back to competing in the Chinese Physics Olympiad (CPhO) in high school, which first made me think seriously about how to model the real world computationally.