About Me

Hello, I'm Peizhong Ju, an Assistant Professor in the Department of Computer Science of University of Kentucky. I earned my Ph.D. degree from Purdue University in 2021 and my B.S. degree from Peking University in 2016.

My research area includes machine learning, smart grid, optimization, and wireless communication. My recent research focuses on theories for explaining the performance of machine learning models. Before that, I worked on power systems and wireless communication. Generally speaking, the goal of my research is to use rigorous mathematical analysis (including probability theory, optimization, game theory, and random matrix theory) to understand the fundamental limits of a complex system under uncertainty and/or disturbance.

You can contact me at peizhong.ju@uky.edu

My CV (as a pdf file) is here (Last updated: Aug 25, 2024).

Check out my Google Scholar profile for a list of my publications.

News

  • I joined the Department of Computer Science of University of Kentucky in August, 2024. Photo
  • On Jul 28, 2024, two of my papers were accepted by MobiHoc'24. One is about Federated Learning, and another is on network-edge classification.
  • On Jan 16, 2024, two of my papers on Reinforcement Learning were accepted by ICLR'24.
  • On Jul 27, 2023, I and Sen Lin presented our continual learning work at ICML'23 at the Hawaii Convention Center, Honolulu.
  • On Jul 20, 2023, The Ohio State University News reported our continual learning research in a press release: "Future AI algorithms have potential to learn like humans, say researchers."
Headshot

Experience


Assistant Professor

2024 - Present Department of Computer Science at University of Kentucky, Lexington, KY, USA

Postdoctoral Scholar

2021 - 2024 ECE department at The Ohio State University, Columbus, OH, USA NSF AI Institute on Future Edge Networks and Distributed Intelligence (AI-EDGE)

Ph.D. in Electrical and Computer Engineering

2016 - 2021 Purdue University, West Lafayette, IN, USA

B.Sc. in Electrical Engineering

2012 - 2016 Peking University, Beijing, China

Honors & Awards

  • Best Paper Award, 13th ACM International Conference on Future Energy Systems (ACM e-Energy 2022)
  • The Bilsland Dissertation Fellowship, Purdue University, 2021
  • Best Paper Award Finalist of 9th ACM International Conference on Future Energy Systems (ACM e-Energy 2018)
  • Outstanding Graduate Award of EE department of Peking University, 2016 (the only one that year)
  • Outstanding Graduate Award of Peking University, 2016
  • IEEE Student Grant, 2015
  • Research Excellence Award, Peking University, 2015
  • China National Scholarship, 2015
  • Samsung Scholarship, 2014
  • Award of excellence, OpenHW2014 open source hardware and embedded computing contest (awarded by ARM and XILINX), 2014
  • EMC Scholarship, 2013

Research

Publications

Conference Papers

  1. Peizhong Ju, Haibo Yang, Jia Liu, Yingbin Liang, and Ness B. Shroff, "Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?" 25th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc'24), 2024 . arXiv version
  2. Chengzhang Li, Peizhong Ju, Atilla Eryilmaz, and Ness B. Shroff, "Efficient Multi-dimensional Compression for Network-edge Classification," 25th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc'24), 2024 . paper
  3. Peizhong Ju, Arnob Ghosh, and Ness B. Shroff, "Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning," 12th International Conference on Learning Representations (ICLR'24), 2024. arXiv version
  4. Yining Li, Peizhong Ju, and Ness B. Shroff, "Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping," 12th International Conference on Learning Representations (ICLR'24), 2024. arXiv version
  5. Sen Lin, Peizhong Ju (co-first author), Yingbin Liang, and Ness B. Shroff, "Theory on Forgetting and Generalization of Continual Learning," 40th International Conference on Machine Learning (ICML'23), 2023. arXiv version
  6. Peizhong Ju, Yingbin Liang, and Ness B. Shroff, "Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning," 11th International Conference on Learning Representations (ICLR'23), 2023. arXiv version
  7. Peizhong Ju, Xiaojun Lin, and Ness B. Shroff, "On the Generalization Power of Overfitted Three-Layer Neural Tangent Kernel Model," 36th Conference on Neural Information Processing Systems (NeurIPS'22), 2022. arXiv version
  8. Peizhong Ju, Xiaojun Lin, and Jianwei Huang, "Distribution-Level Markets under High Renewable Energy Penetration," Proceedings of the 13th ACM International Conference on Future Energy Systems (e-Energy'22), 2022. Best Paper Award open access
  9. Peizhong Ju, Xiaojun Lin, and Ness B. Shroff, "On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models," Proceedings of the 38th International Conference on Machine Learning (ICML'21), PMLR 139:5137-5147, 2021. arXiv version
  10. Peizhong Ju, Xiaojun Lin, and Jia Liu, "Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree," accepted as spotlight presentation at 34th Conference on Neural Information Processing Systems (NeurIPS'20), 2020 (acceptance rate: 20%, spotlight presentations: 3%). spotlight arXiv version
  11. Peizhong Ju, and Xiaojun Lin, "Adversarial Attacks to Distributed Voltage Control in Power Distribution Networks with DERs,", Proceedings of the 9th International Conference on Future Energy Systems (e-Energy'18), pp. 291-302, Karlsruhe, Germany, June 12-15, 2018. Best Paper Award Finalist
  12. Peizhong Ju, Meng Zhang, Xiang Cheng, and Liuqing Yang, "Generalized spatial modulation with transmit antenna grouping for massive MIMO," Proceedings of 2017 IEEE International Conference on Communications (ICC'17), Paris, France, May 21-25, 2017.
  13. Peizhong Ju, Meng Zhang, Xiang Cheng, Cheng-Xiang Wang, and Liuqing Yang, "Generalized spatial modulation with transmit antenna grouping for correlated channels," Proceedings of 2016 IEEE International Conference on Communications (ICC'16), Kuala Lumpur, Malaysia, May 22-27, 2016.
  14. Peizhong Ju, Miaowen Wen, Xiang Cheng, and Liuqing Yang, "An Effective Self-Interference Cancellation Scheme for Spatial Modulated Full Duplex Systems," Proceedings of 2015 IEEE International Conference on Communications (ICC'15), London, UK, June 8-12, 2015.

Journal Papers

  1. Peizhong Ju, Chengzhang Li (co-first author), Yingbin Liang, and Ness B. Shroff (the last two authors represent the entire AI-EDGE faculty team), "AI-EDGE: An NSF AI Institute for Future Edge Networks and Distributed Intelligence." AI Magazine 45.1 (2024): 29-34. open access
  2. Peizhong Ju, Miaowen Wen, Xiang Cheng, and Liuqing Yang, "Achievable-Rate-Enhancing Self-Interference Cancellation for Full-Duplex Communications," IEEE Transactions on Wireless Communications, vol. 17 no. 12, pp. 8473-8484, 2018.
  3. Weilin Qu, Meng Zhang, Xiang Cheng, and Peizhong Ju, "Generalized Spatial Modulation With Transmit Antenna Grouping for Massive MIMO," IEEE Access, vol. 5, pp. 26798-26807, 2017.

Pending Papers

  1. Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff, "Non-asymptotic Convergence of Discrete-time Diffusion Models: New Approach and Improved Rate." arXiv version
  2. Peizhong Ju, Sen Lin, Mark S. Squillante, Yingbin Liang, and Ness B. Shroff, "Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes." arXiv version
  3. Chengzhang Li, Peizhong Ju, Atilla Eryilmaz, and Ness B. Shroff, "Two Levels Are All You Need: Simplifying Data Compression for Timely Edge Classification."

Book Chapters

  1. Peizhong Ju, Xiaojun Lin, and Ness B. Shroff, "On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models," Chapter 3 of Artificial Intelligence for Edge Computing, Springer Nature, August 2023. DOI: 10.1007/978-3-031-40787-1_3

Patents

  1. Xiang Cheng, Peizhong Ju, and Miaowen Wen, "A method of self-interference cancellation scheme for full duplex systems," 2015100550190, State Intellectual Property Office of the People's Republic of China.

Talks & Presentations

  • UK Applied Math Seminar
    Organizer: Department of Mathematics, University of Kentucky
    Location: Lexington, KY
    Date: October 21, 2024
    Title: "Machine Learning and Optimization in Multi-Agent Multi-Objective Frameworks"
  • FutureG Summer Research Camp
    Organizer: DoD Center of Excellence in FutureG, Arizona State University
    Location: Tempe, AZ
    Date: May 7, 2024
    Titles: "Overview of Reinforcement Learning", and "RL at the Edge: Fairness and Action Space Reduction"
  • AI-EDGE SPARKS Seminar
    Organizer: The SPARKS Organizing Committee, The Ohio State University
    Location: Columbus, OH
    Date: March 22, 2024
    Title: "Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning"
  • The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022)
    Organizer: King's College London
    Location: London, UK
    Date: 17-19 December 2022
    Title: "On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model"
  • AIRS in the AIR Academic Series
    Organizer: Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)
    Location: Shenzhen, China
    Date: August 23, 2022
    Title: "Distribution Level Markets under High Renewable Energy Penetration"
  • Long Feng Science Forum
    Organizer: CUHK-Shenzhen
    Location: Shenzhen, China
    Date: August 19, 2022
    Title: "On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models"
  • Joint Workshop between AI-EDGE and IBM
    Organizer: The Ohio State University
    Location: Columbus, OH
    Date: June 15, 2022
    Title: "On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models"
  • TDAI deep learning summer school
    Organizer: The Ohio State University
    Location: Columbus, OH
    Date: June 3, 2022
    Title: "On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models"

Contact

Feel free to reach out to me by email:

peizhong.ju@uky.edu

You can also find me on Google Scholar:

https://scholar.google.com/citations?user=VDzpfOYAAAAJ&hl=en