About Me

I am a third-year graduate student in Computer Science at the University of Maryland, College Park advised by Professor Tom Goldstein. I transferred to the PhD program in Spring 2025 after completing my BS/MS in Computer Science in 2024.

I am broadly interested in the intersection of deep learning, computer vision, and graphics. My recent work focuses on 3D scene reconstruction, and I am currently funded by the IARPA Walk-through Rendering from Images of Varying Altitude (WRIVA) program. Additionally, I have research and teaching experience in multimodal biometric recognition, large language models, vision encoders, generative adversarial networks, image quality metrics, and visualization recommendation.

News

• June 2025: Thank you for coming to our CVPR 2025 poster sessions!
• May 2025: Returned to STR as a Computer Vision Research Intern for the summer.
• February 2025: PUP 3D-GS and Speedy-Splat accepted to CVPR'25!
• January 2025: Transferred to the Computer Science PhD program at UMD! 🎉
• May 2024: Finished my BS/MS in Computer Science at UMD! 🎉

Research Highlights

	Speedy Deformable 3D Gaussian Splatting: Fast Rendering and Compression of Dynamic Scenes Preprint, 2025. PAPER CODE ★ Achieve 10.38× faster rendering, 7.71× smaller model size, and 2.71× shorter training time for dynamic 3D Gaussian Splatting representations through temporal sensitivity pruning and flow-based Gaussian grouping while maintaining visual quality. Allen Tu*, Haiyang Ying*, Alex Hanson, Yonghan Lee, Tom Goldstein, Matthias Zwicker
	Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives CVPR, 2025. PAPER POSTER VIDEO CODE ★ Accelerate 3D Gaussian Splatting rendering speed by over 6× and reduce model size by over 90% through accurately localizing primitives during rasterization and pruning the scene during training, providing a significantly higher speedup than existing techniques while maintaining competitive image quality. Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein
	PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting CVPR, 2025. PAPER POSTER VIDEO CODE ★ Prune 90% of primitives from any pretrained 3D Gaussian Splatting model using a mathematically principled sensitivity score, more than tripling rendering speed while retaining more salient foreground information and higher visual fidelity than previous techniques at a substantially higher compression ratio. Alex Hanson*, Allen Tu*, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker, Tom Goldstein
Predict and Aggregate [1, 2] improves the CMC and ROC performance of the SOTA face recognition system by an empirical upper bound of 11.54% at the evaluation thresholds.	Biometric Recognition and Identification at Altitude and Range (IARPA BRIAR) Systems & Technology Research (STR), 2022-2025. I researched face recognition, body recognition, and multimodal fusion for our SOTA system. Ask me about: Recognizability prediction for face, body, and gait recognition. (2025) Knowledge distillation for face probe-to-gallery feature similarity prediction. (2024) Feature clustering and aggregation for face recognition from videos. (2024) Training operating condition-invariant face encoders. (2023) Model ensembling and mixed-voting classifiers for open search. (2023) Data augmentation via garment transfer for training clothing-invariant body encoders. (2022)

* denotes equal contribution.

Research Experience