Zhenzhe Zheng
Professor
Works at the intersection of economics, learning and systems, including on-device AI agents and strategic AI agents.
Zhenzhe Zheng's lab at SJTU works on computational social science and reasoning & neuro-symbolic AI. 62 papers since 2023, 36 at top venues, mostly KDD and WWW. 3 PhD students and 11 master's students. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 58%
- PhD students
- 3
- MS students
- 11
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 15 papers2026 · T1 7 · collecting
- 16 papers2025 · T1 9
- 14 papers2024 · T1 8
- 17 papers2023 · T1 12
T1 venuesOtherVenues: KDD, WWW, CIKM, WSDM, AAAI
Research focuspaper titles that use each topic's words
- on-device6 papers
- strategic1 paper
- Computational social science0 papers
- Reasoning & neuro-symbolic AI0 papers
- agents0 papers
Paper list
From the lab homepage.
202615 papers
- WWW (T1)A Creator-Aware Recommendation System for Content Platforms
- TONA Device-Cloud Collaborative Data Enrichment Framework for Continual Learning on Devices
- JAIAuction Design with Ex Post ROI Constraints
- INFOCOM (T1)Budget-Constrained Federated Bandits for Mobile Applications
- MobiHocDecision-Focused Learning for Bidding Optimization in Online Advertising
- SIGMETRICSGuiding the Recommender: Information-Aware Auto-Bidding for Content Promotion
- MobiHocJoint Incentive Contract and Assignment Design for Mobile Networked Platforms with Endogenous Supply
- Proceedings of the IEEE International Conference on Distributed Computing SystemsKV Cache Reuse for Elastic LLM Inference on Edge Devices
- KDD (T1)Lethe: Plasticity-aware Active Forgetting for Resource-Efficient On-Device Continual Learning
- SenSys (T1)Optimizing Feature Extraction for On-Device Model Inference with User Behavior Sequences
- SIGMETRICSOptimizing Storage Overhead of User Behavior Log for ML-embedded Mobile Apps
- TONResource-Efficient LLM Customization on Mobile Devices through Proxy Submodel Tuning
202516 papers
- KDD (T1)A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices
- KDD (T1)An Auto-Coupon Framework for Advertiser Retention in Online Advertising
- KDD (T1)ComRecycle: An Intelligent Computation Recycling Framework for Online Advertising
- KDD (T1)Contextual Generative Auction with Permutation-level Externalities for Online Advertising
- MobiHocDRAGON: Enhancing On-Device Model Performance with Distributed Retrieval-Augmented Generation
- WWW (T1)Enabling Real-Time Inference in Online Continual Learning via Device-Cloud Collaboration
- TMCFrom Non-IID to IID: Mobility-aware Hierarchical Federated Learning with Client-Edge Association Control
- ICPPJoint Prediction and Matching for Computing Resource Exchange Platforms
- INFOCOM (T1)Latency-aware Online Continual Learning for Non-Stationary Data Streams
- AAAI (T1)Learning Optimal Auctions with Correlated Value Distributions
- CNMI-VFL: Feature discrepancy-aware distributed model interpretation for vertical federated learning
- TMCMIDDLE: A Mobility-Driven Device-Edge-Cloud Federated Learning Framework
202414 papers
- WSDM (T1)Cost-Effective Active Learning for Bid Exploration in Online Advertising
- MobiCom (T1)Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learning
- WSDM (T1)From Second to First: Mixed Censored Multi-Task Learning for Winning Price Prediction
- SenSys (T1)LiteMoE: Customizing On-device LLM Serving via Proxy Submodel Tuning
- Proceedings of the IEEE International Conference on Distributed Computing SystemsMobility-Aware Device Sampling for Statistical Heterogeneity in Hierarchical Federated Learning
- ICPPNebula: An Edge-Cloud Collaborative Learning Framework for Dynamic Edge Environments
- TONODE: An Online Data Selection Framework for Federated Learning in Mobile Networks
- TMCOn Designing Market Model and Pricing Mechanisms for IoT Data Exchange
- MobiHocOn the Analysis of Two-Stage Stochastic Bandit
- KDD (T1)Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning
- KDD (T1)Robust Auto-Bidding Strategies for Online Advertising
- WWW (T1)Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
202317 papers
- KDD (T1)A Personalized Automated Bidding Framework for Fairness-aware Online Advertising
- WINEAuction Design for Bidders with Ex Post ROI Constraints
- WSDM (T1)Boosting Advertising Space: Designing Ad Auctions for Augment Advertising
- CIKM (T1)CANDY: A Causality-Driven Model for Hotel Dynamic Pricing
- TMCCapitalize Your Data: Optimal Selling Mechanisms for IoT Data Exchange
- IWQoSDistributed Model Interpretation for Vertical Federated Learning with Feature Discrepancy
- CIKM (T1)Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective
- RecSys (T1)Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation
- DASFAAHIT: Learning a Hierarchical Tree-Based Model with Variable-Length Layers for Recommendation Systems
- ICPPLearning From Your Neighbours: Mobility-Driven Device-Edge-Cloud Federated Learning
- WWW (T1)LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation
- CIKM (T1)MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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