Dr. Chengying Huan is a tenure-track Assistant Professor in the School of Computer Science at Nanjing University. He won two second runner ups in ACM-ICPC Asian regional contest and was an ACM-ICPC world finalist. His research focuses on Big Data computing systems, distributed AI training and inference systems, and intelligent data management systems. He has established a strong publication record in prestigious venues, including top-tier conferences and leading journals such as EuroSys, SC, ASPLOS, ICDE, IEEE Transactions on Parallel and Distributed Systems (TPDS), and ACM Transactions on Architecture and Code Optimization (TACO). He actively contributes to the academic community through his service on program committees and as a reviewer for prominent conferences and journals, including ICDE, SC, and TPDS.

Professional Experiences

• Assistant Professor. Nanjing University, Jan 2025 - Now.
• Postdoc. Institute of Software, Chinese Academy of Sciences, Mar 2023 - Dec 2024.
• Head of AI Department. Baihai Inc., Jun 2022 - Mar 2023.

Education

• Ph.D. in Computer Science. Tsinghua University, Sep 2017 - Jun 2022.
• B.Eng. in Software Engineering. Beijing Institute of Technology, Sep 2013 - Jun 2017.

Research Interest

My research interests include:

• Big Data computing systems (Graph processing, ANNS, Matrix completion, etc)
• Distributed AI training and inference systems (LLM training, LLM inference, etc)
• Intelligent Data Management (Context cache, External memory algorithms, Near-Memory computing, etc)

Recent Publications

1. Chengying Huan, Likang Chen, Yongchao Liu, Xuran Wang, Heng Zhang, Shaonan Ma, Yanjun Wu. TeMatch: A Fast Temporal Subgraph Matching Framework with Temporal-Aware Subgraph Matching Algorithms. International Conference on Data Engineering (ICDE 2025).
2. Xiangfei Fang, Chengying Huan, Boying Wang, Shaonan Ma, Heng Zhang, Chen Zhao. HyperSF: A Hypergraph Representation Learning Method Based on Structural Fusion. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2025, Corresponding author).
3. Xiangfei Fang, Boying Wang, Chengying Huan, Shaonan Ma, Heng Zhang, Chen Zhao. HyperKAN: Hypergraph Representation Learning with Kolmogorov-Arnold Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2025).
4. Chengying Huan, Yongchao Liu, Heng Zhang, Shuaiwen Song, Santosh Pandey, Shiyang Chen, Xiangfei Fang, Yue Jin, Baptiste Lepers, Yanjun Wu, Hang Liu. TEA+: A Novel Temporal Graph Random Walk Engine with Hybrid Storage Architecture. ACM Transactions on Architecture and Code Optimization (TACO), 2024.
5. Chengying Huan, Yongchao Liu, Heng Zhang, Hang Liu, Shiyang Chen, Shuaiwen Leon Song, Yanjun Wu. TeGraph+: Scalable Temporal Graph Processing Enabling Flexible Edge Modifications. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2024.
6. Chengying Huan, Shuaiwen Leon Song, Santosh Pandey, Hang Liu, Yongchao Liu, Baptiste Lepers, Changhua He, Kang Chen, Jinlei Jiang, Yongwei Wu. TEA: A General-Purpose Temporal Graph Random Walk Engine. Proceedings of the Eighteenth European Conference on Computer Systems (EuroSys 2023).
7. Rui Zhang, Yukai Huang, Sicheng Liang, Shangyi Sun, Shaonan Ma, Chengying Huan, Lulu Chen, Zhihui Lu, Yang Xu, Ming Yan, Jie Wu. Revisiting Learned Index with Byte-addressable Persistent Storage. Proceedings of the 53rd International Conference on Parallel Processing (ICPP 2023).
8. Yue Jin, Chengying Huan, Heng Zhang, Yongchao Liu, Shuaiwen Leon Song, Rui Zhao, Yao Zhang, Changhua He, Wenguang Chen. G-Sparse: Compiler-Driven Acceleration for Generalized Sparse Computation for Graph Neural Networks on Modern GPUs. Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT 2023).
9. Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu. TANGO: re-thinking quantization for graph neural network training on GPUs. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2023).
10. Chengying Huan, Shuaiwen Leon Song, Yongchao Liu, Heng Zhang, Hang Liu, Charles He, Kang Chen, Jinlei Jiang, Yongwei Wu. T-GCN: A Sampling Based Streaming Graph Neural Network System with Hybrid Architecture. Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT 2022).
11. Chengying Huan, Hang Liu, Mengxing Liu, Yongchao Liu, Changhua He, Kang Chen, Jinlei Jiang, Yongwei Wu, Shuaiwen Leon Song. TeGraph: A Novel General-Purpose Temporal Graph Computing Engine. International Conference on Data Engineering (ICDE 2022).
12. Heng Zhang, Lingda Li, Hang Liu, Donglin Zhuang, Rui Liu, Chengying Huan, Shuang Song, Dingwen Tao, Yongchao Liu, Charles He, Yanjun Wu, Shuaiwen Leon Song. Bring orders into uncertainty: enabling efficient uncertain graph processing via novel path sampling on multi-accelerator systems. Proceedings of the 36th ACM International Conference on Supercomputing (ICS 2022).
13. Mingxing Zhang, Yongwei Wu, Youwei Zhuo, Xuehai Qian, Chengying Huan, Kang Chen. Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2018).

Selected Honors and Awards

1. ACM ICPC World Final, Participation, 2017
2. ACM ICPC Asia Shenyang Regional Contest, Third Place & Gold Medal, 2016
3. ACM ICPC Asia Hong Kong Regional Contest, Second Runner-up, 2016
4. China Collegiate Programming Contest, Gold Medal, 2016
5. China Collegiate Computing Contest (C4) Group Programming Ladder Tournament, First Prize, 2016
6. “Lanqiao Cup” National Software and Information Technology Professionals Competition (C/C Undergraduate A group) Beijing Regional, National first prize (Top 10), 2016
7. Baidu Star World Final, Participation, 2016
8. National Scholarship, Undergraduate Student, 2014-2016