Peizhong's Homepage

About Me

Hello, I'm Peizhong Ju, a Postdoctoral Researcher in Electrical and Computer Engineering at the Ohio State University. 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 ju.171@osu.edu.

My CV (as a pdf file) is here (Last updated: May 10, 2024).

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

News

  • 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


Postdoctoral Scholar

2021 - Present 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

  • Postdoctoral Scholar, the Ohio State University
    Supervisor: Prof. Ness B. Shroff.
    • Multi-agent Reinforcement Learning, Multi-task supervised learning, Generalization Performance Analysis. (Oct. 2021 - Present)
  • Research Assistant, Purdue University
    Advisor: Prof. Xiaojun Lin.
    • Overfitting in Machine Learning (Oct. 2019 - Sept. 2021)
    • Market Design for Renewable in Power Distribution Level (Aug. 2018 - Oct. 2019)
    • Voltage Control in Power Grid with Renewable (Aug. 2016 - Aug. 2018)
  • Undergraduate Research Assistant, Peking University, China
    Advisor: Prof. Xiang Cheng.
    • Antenna Grouping for Massive MIMO (Sep. 2015 - June 2016)
    • Self-Interference Cancellation for Full-Duplex (Oct. 2013 - Sep. 2015)

Publications

Conference Papers

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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%). arXiv version
  9. 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)
  10. 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.
  11. 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.
  12. 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."
  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, Haibo Yang, Jia Liu, Yingbin Liang, Ness B. Shroff, "Understanding the Theoretical Generalization Performance of Federated Learning."
  3. Peizhong Ju, Sen Lin, Mark S. Squillante, Yingbin Liang, and Ness B. Shroff, "Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes." arXiv version
  4. Chengzhang Li, Peizhong Ju, Atilla Eryilmaz, and Ness B. Shroff, "Distributed Linear Dimensionality Reduction Assisted by Centralized NN for 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

  • 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:

ju.171@osu.edu

You can also find me on Google Scholar:

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