Yingli Zhou

Yingli Zhou

ERC postdoc at LIRIS, CNRS / Université Lyon 1. Graph data management for AI.

Yingli ZHOU

I received my Ph.D. in March 2026 from the School of Data Science at The Chinese University of Hong Kong, Shenzhen, advised by Prof. Yixiang Fang. I am a postdoctoral researcher at LIRIS, CNRS / Université Claude Bernard Lyon 1 (Lyon, France) supported by ERC-Advanced Go-Y Project, working with Prof. Angela Bonifati (ACM Fellow, SIGMOD Chair) on graph data management and reliable data infrastructure for AI.

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.

Research Interests

My research agenda is to build causality-aware graph data management and reliable graph data infrastructure for AI. I study how graph structure, causal signals, and scalable data systems can make AI applications more trustworthy, explainable, and efficient.

Current directions include:

  • Causality-aware graph data management: causal property graphs, causal provenance query, and scalable intervention analysis over graph data.
  • Reliable graph data infrastructure for AI: graph-based RAG, structured retrieval, graph memory, and evaluation pipelines for knowledge-intensive AI systems.
  • Scalable graph analytics: efficient algorithms for densest subgraph discovery, community search, clique counting/listing, and graph similarity.
  • Large models for data systems: LLM-powered and pretrained methods for data analysis, database optimization, and DBMS testing.

I am open to collaborations, invited talks, and visiting opportunities in data management, database systems, and graph-based AI infrastructure. Please reach out via email.

Recent News

  • March 2026: I received my Ph.D. from CUHK-Shenzhen and will continue my research as an ERC postdoctoral researcher at LIRIS, CNRS / Université Lyon 1.
  • 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.
  • March 2025: Our paper on the systematic benchmark and analysis of graph-based RAG became available on arXiv. [arXiv]

Selected Open-source Projects

DIGIMON / GraphRAG: the first unified graph-based RAG prototype system for structured retrieval and reasoning over complex data. GitHub Repo stars [papers]
EraRAG: the first graph-based RAG system to handle evolving documents. GitHub Repo stars [arXiv]
BookRAG: a hierarchical structure-aware index-based approach for retrieval-augmented generation on complex documents. GitHub Repo stars [arXiv]
MicroWorld: a lightweight system for turning multi-modal event materials into structured graphs, agent populations, and inspectable social simulations. GitHub Repo stars [site]

Personal Information

  • I have been a big fan of Jay Chou for around 18 years. I also grew up listening to Avril Lavigne and Coldplay, and their music has been a big part of my life.
  • I love watching football, Dota2 and LOL. My favorite national teams are Portugal and France. As for clubs, I support Manchester City and used to support Real Madrid when Cristiano Ronaldo played there.
  • I enjoy running in my free time, and my current goal is to complete a marathon.