Zhenzhe Zheng (郑臻哲）
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Assistant Professor, |
About me
Welcome to my homepage! I am currently a tenure-track assistant professor in the Department of Computer Science and Engineering @ Shanghai Jiao Tong University.
I received my Ph.D. from the CS department @ Shanghai Jiao Tong University in March 2018. My advisors were Prof. Prof. Fan Wu and Prof. Guihai Chen. I was then a postdoctoral researcher in the Coordinated Science Laboratory (CSL) @ UIUC from April 2018 to October 2019, advised by Prof. R. Srikant. I was a visiting student also working with Prof. R. Srikant in the Coordinated Science Laboratory (CSL) @ UIUC from September 2016 to March 2018. I am the recipient of the China Computer Federation (CCF) Excellent Doctoral Dissertation Award in 2018. During my Ph.D. program, I was supported by Google Ph.D. Fellowship and Microsoft Research Asia Ph.D. Fellowship. Prior to my Ph.D., I received my B.E. in Software Engineering from Xidian University in June 2012, and M.S. in Computer Science from Shanghai Jiao Tong University in March 2015.
My research interests are in the intersection of economics, learning and networking. In particular, I have focused on game theory, machine learning and their applications for computer networking, mobile computing and online markets.
Students: I am constantly looking for well motivated students (both undergrad and graduate) who are interested in game theory, machine learning and reinforcement learning, networking and mobile computing, performance analysis and algorithm design, and system implementations. Please email me if you are interested.
Recent News
[2022.10] Paper “Boosting Advertising Space: On Designing Ad Auctions for Augment Advertising” have been accepted to ACM WSDM, 2023. Congratulations to Yangsu!
[2022.09] Technical Report “ODE: A Data Sampling Method for Practical Federated Learning with Streaming Data and Limited Buffer” have been posted on arXiv.
[2022.08] Two Papers “Control-based Bidding for Mobile Livestreaming Ads with Exposure Guarantee” and “Hierarchically Constrained Adaptive Ad Exposure in Feeds” have been accepted to ACM CIKM, 2022. Congratulations to Haoqi, Dagui and Qi!
[2022.07] I will serve as a TPC member for IEEE INFOCOM 2023 and ACM MobiHoc 2023.
[2022.01] Paper “On Designing a Two-stage Auction for Online Advertising” has been accepted to ACM WWW in Economics, Monetization, and Online Markets track, 2022. Congratulations to Yiqing!
[2021.12] Paper ’’Online Data Valuation and Pricing for Machine Learning Tasks in Mobile Health’’ accepted to IEEE INFOCOM 2022. Congratulations to Anran!
[2021.9] Paper ’’A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising’’ accepted to ACM WSDM 2022. Congratulations to Chao!
[2021.9] Paper ’’Capitalize Your Data: Optimal Selling Mechanisms for IoT Data Exchange’’ accepted to IEEE TMC. Congratulations to Qinya!
[2021.6] Paper ’’Data-Free Evaluation of User Contributions in Federated Learning’’ accepted to IEEE WiOpt 2021. Congratulations to Hongtao!
[2021.05] Paper ’’Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising’’ accepted to ACM SIGKDD 2021. Congratulations to Xiangyu!
[2021.05]Paper ’’We Know What You Want: An Advertising Strategy Recommender System for Online Advertising’’ accepted to ACM SIGKDD 2021. Congratulations to Liyi!
[2021.02] Paper ’’Strategy-Proof Online Mechanisms for Weighted AoI Minimization in Edge Computing’’ accepted to IEEE JSAC - Special Issue on Age of Information in Real-time Systems and Networks. Congratulations to Hongtao!.
[2020.11] Paper “Toward Understanding the Influence of Individual Clients in Federated Learning” accepted to AAAI 2021. Congratulations to Yihao!
[2020.10] Paper “Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising” accepted to ACM WSDM 2021.
[2020.10] Paper “On Designing Strategy-Proof Budget Feasible Online Mechanism for Mobile Crowdsensing with Time-Discounting Value” accepted to IEEE TMC.
Research Interests
Game Theory and Network Economics
Computer Networking, Mobile Computing and Online Advertising
Federated Learning and Reinforcement Learning