Leijie (Eric) Wu

Leijie (Eric) Wu

I am too full of life to be half loved

About Me

Welcome to my personal space of interest!

I’m Leijie (Eric) Wu, a Research Scientist in the Theory Lab (Huawei 2012 Labs) at Huawei Hong Kong Research Center (HKRC), which I joined in December 2024.

I received my Ph.D. in Computer Science from the Department of Computing (COMP) at the Hong Kong Polytechnic University (PolyU) on December 11, 2024. I was supervised by Prof. Song Guo, who is currently a Chair Professor in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST).

Before joining PolyU in 2020, I received my Bachelor of Science in Automation at the Beijing Institute of Technology (BIT).

From May to December 2023, I was a Visiting Ph.D. Student in the Scalable Computing Systems Laboratory (SACS Lab) at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where I was supervised by Prof. Anne-Marie Kermarrec from the School of Computer and Communication Sciences (IC).

My research is driven by a central question: how can we build AI systems that remain efficient, adaptive, and reliable under real-world constraints in data, computation, and knowledge? My earlier work focused on federated learning, federated unlearning, and edge intelligence, addressing data privacy, distributed collaboration, and resource constraints. Building on this foundation, my current research centers on agentic retrieval and memory, supported by knowledge graphs and Graph-RAG. I also work on long-context inference through sparse attention and KV-cache optimization, as well as model quantization and deployment. My long-term goal is to develop efficient, knowledge-enhanced, and trustworthy AI systems that bridge advanced learning algorithms with scalable real-world deployment.

At Huawei, I translate research into agents with adaptive retrieval and long-term memory, reliable knowledge systems, and efficient inference infrastructure, with applications in financial services, energy, digital government, and healthcare. My Research Portfolio contains detailed project records and evaluation context; my CV summarizes research, teaching, and academic service.

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