Xijie Huang

Research Engineer at Meta

Pittsburgh, Pennsylvania, United States
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Summary

🤩
Rockstar
🎓
Top School
Xijie Huang is a research engineer focused on algorithm–hardware co-design for efficient deep learning, currently at Meta with nine years of experience bridging research and product-ready systems. He holds a PhD from HKUST and has interned at Snap and Microsoft Research, where he shipped SnapGen—an ultra-efficient T2I model that uniquely demonstrated 1024×1024 image generation on mobile in ~1–2 seconds—and developed RL-based context pruning methods for LLM few-shot learning. His background in electronic and information engineering (Shanghai Jiao Tong, top of class) informs a practical, systems-first approach to model compression and on-device inference. Xijie blends academic rigor with applied impact, repeatedly pushing model efficiency limits to enable high-resolution, low-latency AI on consumer hardware.
code9 years of coding experience
bookDoctor of Philosophy - PhD, Computer Science Engineering (CSE), Doctor of Philosophy - PhD, Computer Science Engineering (CSE) at 香港科技大学
book工学学士学位, 电子信息与电气工程, 89.4/100 (91.3/100 for junior year) Ranking: 2/55, 工学学士学位, 电子信息与电气工程, 89.4/100 (91.3/100 for junior year) Ranking: 2/55 at 上海交通大学
languagesChinese, English
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Github Skills (35)

large-language-models10
action-recognition10
fine-tuning10
lora10
compression10
quantization10
peft9
transformers9
llm9
llama9
natural-language-processing9
rlhf9
ai9
agent9
gpt9

Programming languages (3)

HTMLJupyter NotebookPython

Github contributions (5)

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Contributions:112 pushes, 1 branch in 7 years 6 months
Awesome LLM compression research papers and tools.
Contributions:4 reviews, 38 PRs, 493 pushes in 3 years 1 month
compressionllm
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