About Me

I am a PhD student in Computer Science at the University of Maryland, College Park (UMD), advised by Professor Tom Goldstein. My research spans deep learning, computer vision, and graphics, with emphasis on 3D scene reconstruction, multimodal biometric recognition, and generative priors for robust vision systems. I have published in top machine learning conferences, contributing methods in compression and explainability that enable large-scale recognition and 3D reconstruction in real-world settings. In addition to my academic work, I collaborate with both academic and industry partners on problems spanning vision, language, and generative modeling. I completed my BS/MS in Computer Science at UMD in 2024.

I was a Computer Vision Research Intern at Systems & Technology Research (STR), focusing on multimodal biometric recognition, and a Peer Research Mentor in the FIRE: Capital One Machine Learning program at UMD, where I mentored over 80 undergraduates in their first research experiences. These roles deepened my interest in building machine learning systems that are both efficient and trustworthy. A recurring theme in my work is designing methods that not only achieve high accuracy, but also scale effectively and provide interpretable signals for decision making. My goal is to advance AI that can be deployed in real-world applications, ensuring systems that are safe, reliable, and beneficial to society.

News

🏔️ The SPAR-3D Security, Privacy, and Adversarial Robustness in 3D Generative Vision Models Workshop at CVPR 2026 is now calling for papers and reviewers! ☀️

• December 2025: SPAR-3D accepted as a CVPR 2026 Workshop! 🏔️
• December 2025: TransFIRA accepted to IEEE FG 2026! ⛩️
• December 2025: I joined Rednote (小红书)! 分享科研、PhD life 和假中国人日常。🐼
• December 2025: Released our SplatSuRe paper and code. 📐
• November 2025: Updated our SpeeDe3DGS paper and code. 🚀
• June 2025: Thank you for visiting PUP 3D-GS and Speedy-Splat at CVPR 2025! 🎸

Research Highlights

	TransFIRA: Transfer Learning for Face Image Recognizability Assessment Accepted to IEEE FG, 2026. PAPER CODE WEBSITE Redefine template-based recognition through encoder-grounded recognizability prediction that learns directly from embedding geometry via class-center similarity and angular separation, enabling principled filtering, calibrated weighting, and cross-modal explainability that surpass prior FIQA methods in accuracy, interpretability, and generality. Allen Tu, Kartik Narayan, Joshua Gleason, Jennifer Xu, Matthew Meyn, Tom Goldstein, Vishal Patel
	SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping Preprint, 2025. PAPER CODE WEBSITE Boost DeformableGS rendering speed from 20 to 276 FPS using temporal sensitivity pruning and groupwise SE(3) motion distillation, all while preserving the superior image quality of per-Gaussian neural motion. Allen Tu*, Haiyang Ying*, Alex Hanson, Yonghan Lee, Tom Goldstein, Matthias Zwicker
	SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting Preprint, 2025. PAPER CODE WEBSITE Enhance 3D Gaussian Splatting by selectively injecting super-resolution only where high-frequency detail is missing, yielding sharper results and improved perceptual quality without introducing multi-view inconsistencies. Pranav Asthana, Alex Hanson, Allen Tu, Tom Goldstein, Matthias Zwicker, Amitabh Varshney
	Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives CVPR, 2025. PAPER CODE WEBSITE Accelerate 3D Gaussian Splatting rendering speed by 2× for free by accurately localizing primitives during rasterization and over 6× in total by pruning the scene by more than 90% 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 CODE WEBSITE 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
	IARPA Walk-through Rendering from Images of Varying Altitude (WRIVA) UMIACS, August 2023 — Present. 3D/4D RESEARCH Unconstrained 3D reconstruction and novel view synthesis in challenging real-world environments. Efficient rendering, compression, and training methods for 3D and 4D Gaussian Splatting (3DGS/4DGS) [1, 2, 3] Multi-view consistent super-resolution for training 3DGS with low-resolution imagery Image, video, and multiview diffusion model priors for sparse-view 3D reconstruction Uncertainty quantification algorithms for 3DGS and Neural Radiance Fields (NeRF) PIs: Professor Tom Goldstein, Professor Matthias Zwicker, Professor Abhinav Shrivastava, Dr. Abhay Yadav, Dr. Cheng Peng, Professor Rama Chellappa
	IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) STR, June 2022 — January 2026. Multimodal fusion of incomplete face, body, and gait information in severe operational conditions. Transfer Learning for Face Image Recognizability Assessment (2025) Learned Frame Feature Aggregation for Face Recognition with Low-Quality Video (2024) Operating Condition-Invariant Barlow Twins and Multimodal Ensembling (2023) Style-Based Appearance Flow for Clothing-Robust Body Representation Learning (2022) PIs: Dr. Joshua Gleason, Dr. Jennifer Xu, Dr. Soraya Stevens, Dr. Nathan Shnidman, Dr. Mark Keck, Professor Vishal Patel, Professor Rama Chellappa
	SPAR-3D: Security, Privacy, and Adversarial Robustness in 3D Generative Vision Models CVPR Workshop, 2026. WEBSITE CALL FOR PAPERS The SPAR-3D workshop brings together the 3D vision, AI security, and multimodal reasoning communities to advance robustness, traceability, and trustworthy 3D generative systems. Organizers: Nicole Meng, Yingjie Lao, Francis Engelmann, Ethan Rathbun, Shaoyi Huang, Allen Tu, Josué Martínez-Martínez, Dongjin Song, Faysal Hossain Shezan, Renjie Wan

* denotes equal contribution. [BibTeX]

Experience