I am a researcher at the University of Maryland, College Park, focused on the intersection of computer vision, geometry processing, and geospatial data analysis. My research centers on neural representations of geospatial data, combining implicit neural modeling, topology-aware analysis, and generative frameworks to enable interpretable, continuous, and scalable representations of the physical world. I am motivated by interdisciplinary collaboration that bridges machine learning, graphics, and scientific computing.
During my BEng and MPhil at Hong Kong University of Science and Technology, I was gratefully advised by Prof. Long Quan in 3D computer graphics and vision. Currently, I am gratefully advised by Prof. Leila De Floriani.
Please reach out to chat and collaborate π!
“Topology is precisely the mathematical discipline that allows the passage from local to global.” β RenΓ© Thom
PhD in Computer Science
University of Maryland, College Park
MPhil in Computer Science
Hong Kong University of Science and Technology
BEng Dual Major in Computer Science Engineering and Electronic and Computer Engineering
Hong Kong University of Science and Technology

Accepted at CVPR 2026 (main conference). See you in Denver!
Jun 1, 2026

Accepted at CVPR 2026 Workshop PVUW. This work studies how to make VideoLLMs more camera-aware by benchmarking camera motion understanding and injecting geometry-derived motion cues at inference time.
Mar 13, 2026
Jan 1, 2026

We are actively working on the formating of this paper.
Sep 15, 2024