About Me
I am a postdoctoral fellow in the Department of Electrical and Computer Engineering at the University of Toronto, working with Prof. Hans-Arno Jacobsen. My research focuses on scalable graph data analytics and graph learning, with applications to cloud and microservice systems, agentic AI, and scientific discovery. I am particularly interested in graph-augmented root-cause analysis, the reliability of multi-agent systems, and quantum computing methods for data management.
I received my Ph.D. in Computer Science from The Hong Kong Polytechnic University in 2025, under the supervision of Prof. Jieming Shi. My doctoral research developed efficient and scalable algorithms for clustering and embedding attributed graphs and hypergraphs. Before that, I earned my B.Eng. in Software Engineering from Nanjing University in 2019.
Updates
- 2026-08-24 I received the COMP PhD Thesis Merit Award of 2026 from Department of Computing, The Hong Kong Polytechnic University.
- 2026-07-07 Our tutorial How Can Quantum Computing and Databases Meet "in Practice"? From Algorithms to Systems is accepted to the VLDB 2026 Tutorial Track.
- 2026-07-02 Two papers are accepted to VLDB 2026. Congrats to Gongyao Guo and Qiao He!
- 2026-05-11 Our short paper Quantum Hypergraph Partitioning is accepted to Q-Data workshop, collocated with SIGMOD 2026. Read the paper here.
- 2026-05-02 I am giving a talk titled Quantum Optimization for Sustainable Data Management at the DSDS workshop, collocated with ICDE in Montreal.
- 2025-11-10 I am joining the MSRG group at the University of Toronto as a postdoctoral fellow, working with Prof. Hans-Arno Jacobsen.
- 2025-08-15 I successfully defended my PhD thesis! The title of my thesis is Advancing Clustering and Embedding for Attributed Network Structures.
- 2025-06-22 Our paper Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges is accepted to VLDB 2025.
Publications
- Hanwen Liu, Yiran Li, Ibrahim Sabek, Xuanhe Zhou, Hans-Arno Jacobsen. "How Can Quantum Computing and Databases Meet "in Practice"? From Algorithms to Systems." VLDB 2026 Tutorial.
- Gongyao Guo, Chen Feng, Yiran Li, Jieming Shi. "Efficient GPU-Accelerated Adaptive Minimum Cost Seed Selection." VLDB 2026.
- Qiao He, Yiran Li, Man Lung YIU, Jieming Shi. "Efficient GPU-Accelerated Local Subgraph Counting." VLDB 2026.
- Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Yiran Li, Hans-Arno Jacobsen. "GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices." ASE 2026. [arXiv] [Artifacts]
- Chen Feng, Gongyao Guo, Yiran Li, Jieming Shi, Sibo Wang. "Efficient Approximation Algorithms for Adaptive Minimum Cost Seed Selection via mRR-set Updates." KDD 2026. [Paper] [Code]
- Yiran Li, Y. Batuhan Yilmaz, Michael Silver, Zachary Vernec, Hans-Arno Jacobsen. "Quantum Hypergraph Partitioning." Q-Data 2026 (SIGMOD workshop). [arXiv] [Code]
- Yiran Li, Gongyao Guo, Chen Feng, Jieming Shi. "Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges." VLDB 2025. [arXiv] [Code]
- Yiran Li, Gongyao Guo, Jieming Shi, Sibo Wang, Qing Li. "Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding." ICDE 2025. [arXiv] [Code]
- Zhihao Ding*, Ting Zhang*, Yiran Li, Jieming Shi, Chen Jason Zhang. "RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction." AAAI 2025. [arXiv] [Code]
- Yiran Li, Gongyao Guo, Jieming Shi, Renchi Yang, Shiqi Shen, Qing Li, Jun Luo. "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation." The VLDB Journal 2024. [arXiv] [Technical report] [Code]
- Yiran Li, Renchi Yang, Jieming Shi. "Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation." SIGMOD 2023. [Presentation video] [Code]
Projects
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