🎓Biography
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 😆!
“Topology is precisely the mathematical discipline that allows the passage from local to global.” — René Thom
- Spatial Representation Learning
- Neural Rendering
- Topological/Morphological Analysis
- Information Retrieval
- Generative Model
- Data Visualization
- AI4Science
- Vision-Language Model
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
- [May 2026] New preprint Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data is out on arXiv — benchmarking amortized neural representations on terrain heightfields and proposing HUVR+SIREN for a compact terrain neural format. 🗻
- [May 2026] New preprint ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation is out on arXiv — a follow-up to ImplicitTerrain toward a compact, efficient neural terrain data format. 🏔️
- [May 2026] Our paper A Parallel Scale-Space Method for Critical Features Tracking on Triangulated Irregular Networks is accepted at ACM Transactions on Spatial Algorithms and Systems (TSAS). 📄
- [Mar. 2026] Our paper Geometry-Guided Camera Motion Understanding in VideoLLMs, developed during my 2025 summer internship at Dolby Laboratories – Advanced Technology Group (ATG), is now accepted @CVPR2026 Workshop PVUW. 🎬
- [Mar. 2026] Our paper SASNet: Spatially-Adaptive Sinusoidal Networks for INRs is accepted at CVPR 2026. See you @Denver! ✨
- [Aug. 2025] Completed a rewarding summer journey as a PhD Research Intern at Dolby Laboratories – Advanced Technology Group (ATG). Grateful to my mentor Harsha Musunuri and manager Guan-ming Su for their guidance and support throughout this internship. 🚀
- [Oct. 2024] Our paper scale space for TINs received the best paper runner-up at ACM SIGSPAITAL2024. 🏆
- [Aug. 2024] We have one paper accepted as Oral presentation at ACM SIGSPAITAL2024 See you @Georgia. 🎤
- [Apr. 2024] Our paper ImplicitTerrain is accepted as Oral presentation at INRV2024 (CVPR2024). See you @Seattle. 🌄
- [Aug. 2021] First day at University of Maryland, College Park, working with Prof. Leila De Floriani. 🎓
SIRENs are powerful implicit neural representations, but they are highly sensitive to frequency initialization: low frequencies over-smooth fine detail, while high frequencies inject noise into smooth regions. SASNet addresses this with a fixed frequency embedding paired with jointly learned spatially-adaptive masks that localize each sinusoidal neuron’s influence across the domain. The result is sharper reconstructions, less noise, and faster convergence across image fitting, 3D volumes, and SDF reconstruction — consistently outperforming INR baselines.
Jun 1, 2026
A controlled benchmark of amortized neural representations on high-resolution (1 m/pixel) terrain elevation data, and HUVR+SIREN — a hypernetwork with a smooth, analytically differentiable decoder that attains the best height and derivative fidelity with no extra per-tile storage.
May 29, 2026
A follow-up to (CVPR 2024 INRV Workshop), advancing terrain INRs toward a compact, efficient neural terrain data format with wavelet-guided spatial adaptivity, derivative-aware supervision, and post-training compression.
May 21, 2026
Published in ACM Transactions on Spatial Algorithms and Systems (TSAS). This is the extended journal version of our SIGSPATIAL'24 conference paper , with new contributions in scale-aware seam-free sampling (S3), improved TIN smoothing, and a fully parallel GPU implementation of critical point tracking.
May 8, 2026
