Yanyan Shen (沈艳艳)

Professor
School of Computer Science
Shanghai Jiao Tong University

Email: shenyy [AT] sjtu.edu.cn

Bio

Yanyan is currently a professor at the School of Computer Science, Shanghai Jiao Tong University (SJTU). She received her bachelor degree from Peking University (PKU), and obtained her doctoral degree from National University of Singapore (NUS). Her broad research interests include data management and machine learning, with a central belief that data is the foundation of everything. Guided by this belief, she focuses on developing efficient and automated data management solutions to facilitate both data analytics and agentic systems, across various data-driven application domains.

Yanyan has won a few awards, including ICDE 2023 best paper award, PVLDB 2022 best research paper award, DASFAA 2019 best paper runner-up, DASFAA 2024 best student paper award, APWeb-WAIM 2018 best student paper award, etc. She has served as a PC member of top international conferences such as SIGMOD, PVLDB, ICDE, KDD and has been selected as VLDB 2023 and 2024 Distinguished Associate Editor, VLDB 2019 Distinguished Reviewer, ICDE 2019 Outstanding Reviewer. She has been invited to serve as Associate Editor of IEEE TKDE, VLDB Journal, and PVLDB 2023/2024/2026.

📢 I am looking for PhD and master students enrolled in Fall 2027. If you are strongly committed to research and interested in data management for agentic AI, please send your CV.

Research Interests

• Data x AI (training, post-training, inference)
• Complex Data Analytics with distributed systems and agentic AI
• Responsible Machine Learning

Recent Selected Publications

See full publications in Google Scholar. The code repositories for the recent papers are available here.

• Yanyan Shen, X. Sean Wang, Xiaoyong Du, Beng Chin Ooi, Hong Mei. Entity-Centric Data Management for the Ubiquitous Computing Era. SCIENCE CHINA Information Sciences (SCIS), 2026.
• Yifei Xu, Yanyan Shen, et al. HEXA: A Disjoint-Subgraph-Based Indexing Framework for Approximate Nearest Neighbor Search at Billion Scale. PVLDB, 2026.
• Xin Zhang, Yanyan Shen, et al. Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling. PVLDB, 2026.
• Jingzhi Fang, Yanyan Shen, et al. Mil: Cost-guided Minimum Makespan Scheduling for Applications of Multiple LLMs. PVLDB, 2026.
• Jingxuan He, et al. CoShap: A Scalable Coalition Growth Approach to Shapley Value Approximation. SIGMOD, 2026.
• Jiale Deng, Yanyan Shen, et al. DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors. KDD, 2026.
• Qing Li, Yanyan Shen, et al. LCATS: LLM-Guided Constraint-Aware Tabular Data Synthesis. KDD, 2026.
• Jiale Deng, Yanyan Shen, et al. Influence Guided Context Selection for Effective Retrieval-Augmented Generation. NeurIPS, 2025.
• Lifan Zhao, Yanyan Shen, et al. Less is More: Unlocking Specialization of Time Series Foundation Models via Structured Pruning. NeurIPS, 2025.
• Shihong Gao, Xin Zhang, Yanyan Shen, et al. Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving. SIGMOD, 2025.
• Jiaqi Zhu, Shaofeng Cai, Yanyan Shen, et al. In-Context Adaptation to Concept Drift for Learned Database Operations. ICML, 2025.
• Lifan Zhao, Yanyan Shen. Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting. KDD, 2025.
• Qiqi Zhou, Yanyan Shen, et al. Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation. VLDB, 2025.
• Yanyan Shen, et al. Efficient Training of Graph Neural Networks on Large Graphs tutorial. VLDB, 2024.
• Shihong Gao, et al. SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement. SIGMOD, 2024.
• Jingzhi Fang, et al. STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically. SIGMOD, 2024.
• Lifan Zhao, Yanyan Shen. Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators. ICLR, 2024.
• Jiale Deng, Yanyan Shen. Self-Interpretable Graph Learning with Sufficient and Necessary Explanations. AAAI, 2024.
• Jinyong Fan, Yanyan Shen. StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting. AAAI, 2024.
• Tong Li, et al. MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. AAAI, 2024.
• Shihong Gao, et al. ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs. In Proceedings of the VLDB Endowment (PVLDB), 2024.
• Lifan Zhao, Shuming Kong, Yanyan Shen. DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting. KDD, 2023.
• Tong Li, Jiale Deng, Yanyan Shen, Luyu Qiu, Yongxiang Huang, Caleb Chen Cao. Towards Fine-grained Explainability for Heterogeneous Graph Neural Network. AAAI, 2023.
• Yiming Li, Yanyan Shen, et al. Orca: Scalable Temporal Graph Neural Networks Training with Theoretical Guarantees. SIGMOD, 2023.
• Xin Zhang, Yanyan Shen, et al. DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with GPU. SIGMOD, 2023.
• Jia Li, Yanyan Shen, et al. SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation. SIGMOD, 2023.
• Yiming Li, Yanyan Shen, et al. Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank. VLDB, 2023.
• Zhikai Wang, Yanyan Shen. Incremental Learning for Multi-Interest Sequential Recommendation. ICDE, 2023.
• Shuming Kong, Yanyan Shen, et al. Resolving Training Biases via Influence-based Data Relabeling. ICLR oral, 2022.

Professional Service

Journal Associate Editor

• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• VLDB Journal (VLDBJ)

Journal Guest Editor

• VLDB Journal (Special Issue on Data Science for Responsible Data Management 2021)
• ACM/IMS Transactions on Data Science (Special Issue on Data Science for Next-generation Big Data 2021)
• Data Science and Engineering (Special Issue on DASFAA 2020)

Journal Reviewer

• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• ACM Transactions on Information Systems (TOIS)
• IEEE Transactions on Parallel and Distributed Systems (TPDS)
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• IEEE Transactions on Computers (TC)
• IEEE Transactions on Intelligent Transportation Systems (TITS)
• ACM/IMS Transactions on Data Science (TDS)

Conferences Program Committee

• ACM International Conference on Management of Data (SIGMOD): 2021, 2023, 2024, 2026 (Demo-track)
• PVLDB Review Board: 2019, 2020, 2022, 2023 (Associate Editor), 2024 (Associate Editor), 2025, 2026 (Associate Editor), 2027
• IEEE International Conference on Data Engineering (ICDE): 2018, 2019, 2020, 2022, 2023 (Demo Co-chair), 2025, 2027
• ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD): 2019, 2020, 2021, 2022, 2025, 2026
• IEEE International Conference on Big Data: 2022 (PC Vice-Co-Chair)
• AAAI Conference on Artificial Intelligence (AAAI): 2019, 2020, 2021, 2022, 2027 (SPC)
• International Joint Conference on Artificial Intelligence (IJCAI): 2018, 2019, 2020
• ACM Symposium on Cloud Computing (SOCC): 2020
• ACM International Conference on Information and Knowledge Management (CIKM): 2019
• SIAM International Conference on Data Mining (SDM): 2021
• Database Systems for Advanced Applications (DASFAA): 2017, 2018, 2019, 2020, 2021

Teaching Activities

• Computer Architecture, CS Undergraduate
• Database Principles, CS Undergraduate
• Programming Practice and Problem Solving, CS Undergraduate
• English Academic Practice, CS Graduate