Ph.D. student at Yale University researching computer vision and machine learning. I build systems that understand, reconstruct, and predict dynamic 3D scenes.
Background & Interests
I'm a Ph.D. student in Computer Science at Yale University, advised by Professor Alex Wong. My research lies at the intersection of 3D computer vision, neural rendering, and deep learning.
I'm particularly excited about developing methods that can understand and predict the dynamics of real-world 3D scenes. My recent work focuses on combining neural scene representations like 3D Gaussian Splatting with sequence modeling to enable temporal extrapolation—predicting how scenes will evolve beyond observed data.
Before Yale, I completed my B.S. in Computer Science with a minor in Neural Computation at Carnegie Mellon University (2023), where I worked with Professor Tai-Sing Lee on computational neuroscience and neural network modeling of visual cortex.
🎉 NSF Graduate Research Fellowship (2025)
Honored to receive support for my research in 3D representation learning.
Areas I'm actively exploring
Novel view synthesis and dynamic scene reconstruction
Implicit representations for 3D understanding
Monocular and multi-view depth prediction
Bridging vision, language, and touch modalities
Selected work in top-tier venues
Applying research to real-world problems
Computer Vision Intern
Computer Vision Intern
Computer Vision Intern
Reviewer for CVPR (2025, 2026), ICLR (2025, 2026), NeurIPS (2024, 2025)
I'm actively seeking research scientist and ML engineer positions in industry. If you're working on challenging problems in 3D vision, neural rendering, or foundation models, I'd love to hear from you!