Research interests
Learning objectives, representations, and architectures for reusable knowledge, multimodal and
spatial reasoning, and generalization.
Education
University of Illinois Urbana-Champaign
B.S. in Statistics and Computer Science
Expected May 2027
GPA: 3.92 / 4.00
Technical skills
Fluent: Python, PyTorch · Familiar: TensorFlow, CUDA · Basic: C/C++
Research experience
Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
May 2024–October 2025
Research mentor: Shiji Zhou · Co-first author and NeurIPS 2025 poster presenter
- Formulated competing objectives as constrained optimization and derived an implicit gradient-surgery update requiring one backpropagation per step.
- Implemented and maintained the complete PyTorch codebase as the sole code contributor, including data, training, logging, and evaluation pipelines.
- Led classification and image-generation experiments, ablations, analysis, and time-sensitive rebuttal experiments; co-wrote core parts of the reviewer response.
Self-Supervised Learning for 3D Scene Representation
June 2024–December 2025
Collaboration with Sipeng Zhang and members of NVIDIA Spatial Intelligence Lab / NuRec
- Proposed masked multi-view feature prediction using DINOv3 features to learn cross-scene, view-consistent 3D scene representations.
- Designed masking, camera-pose and spatiotemporal/positional embeddings, and a ViT-based predictor; implemented the end-to-end PyTorch pipeline.
- Built a working prototype and integration interface and ran initial validation with collaborators.
Selected technical projects
- GPT-2 and FlashAttention CUDA Kernels. Implemented GPT-2 inference and FlashAttention kernels in CUDA/C++ and validated outputs against a reference implementation.
- ImageNet CNN Training Pipeline. Built an end-to-end PyTorch pipeline and trained an AlexNet-style CNN on 10% of ImageNet-1k in under 24 hours using one 6 GB RTX 3060 laptop GPU, reaching 29.8% top-1 and 50.2% top-5 accuracy on the full validation set. [Code]