About
I'm a Computer Science student at SUSTech (GPA 3.79/4.0) researching physics-informed machine learning, surgical vision, and data-driven decision systems. I currently develop multitemporal crop yield pipelines as a Mitacs & CSC funded research intern at McGill University and previously built analytics products at Wego Singapore showcased at WiT conferences in Dubai and Japan. My projects explore frontier intersections of AI and physical systems—from Wave-CDAnet, a physics-informed neural network for water wave super-resolution, to event-based depth estimation for ophthalmic surgery and biomimetic underwater robotics funded by municipal innovation grants.
Work Experience
Skills
Check out my latest work
I've worked on a variety of projects, from simple websites to complex web applications. Here are a few of my favorites.
Wave-CDAnet: Physics-Informed Neural Network for Water Wave Modeling
Developed a physics-informed neural network that combines a 3D U-Net encoder with an implicit decoder and Rayleigh–Bénard PDE residual regularization to recover multi-scale wave dynamics at super-resolution.
AI-Powered 3D Visualization System for Ophthalmic Surgery
Implemented event-based depth estimation and monocular 3D reconstruction for ophthalmic surgery video, coordinating AI engineers and clinicians to deliver clinical review prototypes backed by 10,000 RMB innovation funding.
I like building things
I enjoy building things and collaborating on innovative projects. Check out my projects section to see some of my recent work.
Get in Touch
Want to chat? Feel free to send me an email or connect on LinkedIn. I'll respond whenever I can.