Resume

University of California, Los Angeles (UCLA)

University of California, Los Angeles (UCLA)

• GPA: 3.92/4.0 (undergraduate)
• Dean's Honors List, 2021–2025
• Relevant coursework: Efficient Deep Learning, Advanced Deep Learning, Machine Learning, Computer Vision, Natural Language Processing, Reinforcement Learning, Algorithms, Linear Algebra, Probability & Statistics

Quadric

• Implemented Qwen3.6 linear-attention kernels from scratch for prefill and decode on the Chimera GPNPU, supporting production inference alongside MoE blocks.
• Optimized linear-attention prefill/decode and hardware-level routing, achieving a 3.5× decode speedup.
• Optimized matmul and GEMM kernels for the GPNPU, achieving a 7.6× matmul efficiency improvement.
• Improved FX64 square-root and division implementations, resolving precision-scaling issues to improve numerical accuracy in edge cases.

UCLA, Bolei Zhou Lab · Advisor: Prof. Bolei Zhou

• Developed a vision-language-action (VLA) pipeline for humanoid manipulation, combining VLM-based instruction following and imitation learning with demonstrations collected through VR teleoperation.
• Automated the URBAN-SIM asset pipeline with GPT-4o, Grounded DINO, and SAM, supporting 10,000+ interactive 3D assets for procedural obstacle placement.
• Led sim-to-real deployment on COCO robots, evaluating navigation policies across 50+ unseen urban scenarios.

UCLA, Computational Machine Learning Lab · Advisor: Prof. Cho-Jui Hsieh

• Developed a training-free fingerprinting method using layer-wise memorization profiles; studied embedding-space selection for diffusion and GAN models with ViT and CNN encoders.

• Optimized Qwen3-VL-2B inference on Snapdragon 8 Elite, achieving a 2.05× speedup over the project baseline.
• Configured llama.cpp with importance-matrix-based Q4_0 quantization, memory locking (mlock), and CPU affinity for on-device inference.
• Fine-tuned with LoRA for visual question answering, evaluating both task quality and inference performance.

• Built an asynchronous C++ web server using Boost.Asio, Redis caching, and PostgreSQL connection pooling to support 1,000+ concurrent users.
• Led a four-person team using TDD; automated CI/CD with Docker and Google Cloud Build.

Jack He, Jianxing Zhao, Andrew Bai, Cho-Jui Hsieh

Wayne Wu et al. (co-author)

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Wayne Wu et al. (co-author)

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