Allen Tu

Portrait

atu1@umd.edu



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

Research Highlights



Achieve 10.38Ɨ faster rendering, 7.71Ɨ smaller model size, and 2.71Ɨ shorter training time for dynamic 3D Gaussian Splatting representations through temporal sensitivity pruning and flow-based Gaussian grouping while maintaining visual quality.





Accelerate 3D Gaussian Splatting rendering speed by over 6Ɨ 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.





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.



Zoomable Image

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:
  1. Recognizability prediction for face, body, and gait recognition. (2025)
  2. Knowledge distillation for face probe-to-gallery feature similarity prediction. (2024)
  3. Feature clustering and aggregation for face recognition from videos. (2024)
  4. Training operating condition-invariant face encoders. (2023)
  5. Model ensembling and mixed-voting classifiers for open search. (2023)
  6. Data augmentation via garment transfer for training clothing-invariant body encoders. (2022)

* denotes equal contribution.

Research Experience

Systems & Technology Research (STR)
Video and Image Understanding Group
  • Peer Research Mentor: January 2021 — December 2022
    Advisor: Dr. Raymond Tu