Efficient AI Systems · Edge Intelligence · LLM Adaptation
Ziming (Phoenix) Dai
Ph.D. Student, City University of Hong Kong
I work on efficient, adaptive, and deployable AI systems, with a focus on federated and edge intelligence, large model customization, and industrial model enhancement under privacy, latency, and resource constraints.
Current Focus
My current research explores how AI models can be customized, compressed, and upgraded for real-world distributed and industrial environments while remaining efficient, robust, and practical under privacy, latency, and resource constraints.
Experience
Academic Experience
City University of Hong Kong
Ph.D. Student in Data Science, advised by Prof. Zimu Zhou.
Tianjin University
M.Eng. in Computer Technology at Edge Big Bang Lab, advised by Prof. Chao Qiu and Prof. Xiaofei Wang.
Tianjin University
B.Eng. in Artificial Intelligence. Awarded Outstanding Undergraduate Thesis.
Internship Experience
Qfin Holdings (360 DigiTech)
NLP Engineer Intern, working on industrial language models and model enhancement.
Paiou Cloud Computing (Shanghai)
Algorithm Engineer Intern, working on model algorithms and applied AI systems.
Beijing Wenge Technology
Algorithm Engineer Intern, focusing on named entity recognition for low-resource languages.
Selected Publications
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