<aside> 🧠 Hi there! Chi Xing is a M.Sc student in Artificial Intelligence at University of Edinburgh. His research interests lie in the intersection of Machine Learning Framework, Distributed Computer System and Computer Vision, with a focus on developing serverless generative model service and exploring their theoretical and practical aspects. He has a solid background in both research and engineering, having obtained a B.Sc degree in Computer Science with 1st class honors from University of Liverpool.

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🎓 Education

M.Sc Artificial Intelligence - Informatics@University of Edinburgh | 2024

• Focused on various machine learning frameworks, ranging from basic neural networks (RNN, CNN, MLP, etc.) to advanced modern frameworks (Transformers, Diffusion Models, Large Multi-Modality Models, etc.)
• Dissertation is working on accelerating and serverless-supported preference alignment techniques (such as LoRA fine-tuning, RLHF, DPO and SFT, etc.). This project is supervised by Prof. Luo Mai.

B.Sc Computer Science - University of Liverpool | 2020

• Research Interest Points: Algorithm Design, C++/C/C#, Optimisation, Machine Learning, AI Safety, Java, Web Development.
• Dissertation is focused on exploring various scheduling algorithms for modern smart grid. This project is supervised by Prof. Prudence Wong.

🚀 Experience

Core Contributor, Reviewer - ServerlessLLM | November 2024 - Present

• Support ServerlessLLM deployment on SLURM-based HPC
• Contributed to a scalable, cost-efficient LLM (LoRA) fine-tuning solution.
• Gained real-world exposure to AI infrastructure engineering beyond just model training.

LLM Researcher - N8 CIR | June 2024 - September 2024

• Focused on benchmarking various LLMs for reading biomedical literature, utilizing Llama.cpp to quantize open-source models such as Llama3.1-70B, Llama3.1-405B, DBRX, and Mixtral-8x22B.
• Developed an objective scoring system that extracts key information from model outputs and evaluates their similarity to manually extracted data for performance benchmarking.
• Designed a summarization method to reduce input size, enabling the use of models with smaller context windows.
• This work also involved comparing model performance across different hardware platforms, including NVIDIA GH200, A100, and CPU/GPU references, and deploying LLMs on high-performance computing (HPC) architectures.

Core Contributor, OSPP Mentor - Casibase | January 2024 - August 2024