Research Intern at University of Illinois Urbana-Champaign
Illinois, United States
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Summary
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Rockstar
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Top School
Junwei Deng is a data-centric AI researcher and software engineer with eight years of experience building AI foundations and production ML systems across Intel and research labs. Currently a Ph.D. student in Information Sciences at UIUC and a Research Assistant, he blends deep academic training with industry practice—previously core-developing BigDL LLM tooling and improving performance and APIs at Intel and contributing documentation to the widely used intel/ipex-llm project. He has interned and collaborated with major tech teams at Microsoft and Google, and brings strong bench skills in benchmark design, API implementation, and customer-facing technical communication. Junwei’s background in electrical and computer engineering plus an MS in Data Science informs a pragmatic approach to model infrastructure, and his documentation-driven contributions show a rare focus on usability and developer experience in low-level LLM tooling.
8 years of coding experience
3 years of employment as a software developer
Master of Science in Information, Data Science, Master of Science in Information, Data Science at University of Michigan
Doctor of Philosophy - PhD, Information Sciences, Doctor of Philosophy - PhD, Information Sciences at University of Illinois Urbana-Champaign
Bachelor of Science - BS, Electrical and Computer Engineering, Bachelor of Science - BS, Electrical and Computer Engineering at Shanghai Jiao Tong University
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.
Role in this project:
Technical Writer
Contributions:1095 reviews, 261 commits, 624 PRs in 1 year 8 months
Contributions summary:Junwei primarily focused on updating and refactoring documentation related to the "Chronos" component within the repository. Their commits involved revising user guides, fixing broken links, correcting typos, and restructuring the document organization. The contributions enhanced the clarity and usability of the documentation, including updates for API documentation and the addition of quick tour information. Overall, the user's work significantly improved the documentation quality and user experience.
BigDL: Distributed Deep Learning Framework for Apache Spark
Contributions:494 pushes, 195 branches in 1 year 8 months
apache-sparkbigdldeep-learning
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