Yingli ZHOU
I will obtain my Ph.D. in late March 2026 from the School of Data Science at The Chinese University of Hong Kong, Shenzhen, advised by Prof. Yixiang Fang. After that, i will join Université Claude Bernard Lyon 1 (Lyon, France) as a postdoctoral researcher, supported by the ERC Advanced Grant GO-Y project, affiliated with CNRS LIRIS, where I will work with Prof. Angela Bonifati (ACM Fellow, SIGMOD Chair).
I received my M.Eng. and B.Eng. from Harbin Institute of Technology in 2022 and 2020 respectively, under the supervision of Prof. Yunming Ye. I was a visiting student at the National University of Singapore in 2025, where I worked with Prof. Xiaokui Xiao.
My research lies at the intersection of data management, graph mining, large language models, and causal inference on graph data. I am broadly interested in graph-based RAG systems, LLM-powered data systems, causal property graph modeling, and scalable graph analytics.
I am open to collaborations, invited talks, and visiting opportunities. Please reach out via email.
Research Interests
My research focuses on algorithms and systems for Big Data Management and Data Science. Current interests include Causality-aware Data+AI analytics algorithms and systems, covering topics such as causal provenance query, retrieval-augmented generation (RAG), LLM-based data analysis, graph analytics, and scalable data processing systems.
Recent News
- February 2026: Two papers were accepted to SIGMOD 2026.
- January 2026: One paper was accepted to VLDB 2026.
- September 2025: One paper was accepted to VLDB 2026.
- August 2025: One paper was accepted to SIGMOD 2026, and one paper was accepted to VLDB 2026.
- June 2025: Our paper “Efficient k-Clique Densest Subgraph Discovery: Towards Bridging Practice and Theory” was accepted to VLDB 2025.
- March 2025: Our paper on the systematic benchmark and analysis of graph-based RAG became available on arXiv. [arXiv]