Ming Shi

I am a Post-Doctoral Scholar of the Department of Electrical and Computer Engineering and NSF AI-EDGE Institute at The Ohio State University, Columbus, OH, USA. I am fortunate to be advised by Prof. Ness Shroff and Prof. Yingbin Liang. I received my Ph.D. degree from Elmore Family School of Electrical and Computer Engineering, College of Engineering at Purdue University, West Lafayette, IN, USA, in 2022. I was fortunate to be advised by Prof. Xiaojun Lin. Email: shi.1796 [at] osu [dot] edu or sming [at] purdue [dot] edu I will join the Department of Electrical Engineering at the University at Buffalo (SUNY Buffalo) as an assistant professor in fall 2024.

Research interests

My research strives to develop AI-powered autonomous networked systems with strong theoretical performance guarantees under uncertainty and practical domain-specific constraints.



• Reinforcement learning, bandit learning, and generative AI

• Online (non-)convex optimization and online control

• Networking, wireless, and Edge-AI

• Power systems and economics

• Theoretical analysis

News and Honors

• Invited talk: “Autonomous Networked Systems: Adversarial Online RL Under Limited Defender Resources”

  - at New York University (NYU), New York, November 2023.
  

• Invited talk: “AI-Powered Autonomous Systems with Switching Costs: Power-of-2-Arms”

  - at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, July 2023.
  

• Invited talk: “Near-Optimal Adversarial Reinforcement Learning with Switching Costs”

  - at the California Institute of Technology (Caltech), Pasadena, California, July 2023.
  

• Invited talk: “RL under Instantaneous Hard Safety Constraints and POMDPs: From Wireless Communications to Smart Health”

  - at Northeastern University (NEU), Boston, Massachusetts, July 2023.
  

• Conference presentation: “A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints”

  - at International Conference on Machine Learning (ICML), Honolulu, Hawaii, July 2023.
  

• Conference presentation: “Near-Optimal Adversarial Reinforcement Learning with Switching Costs”

  - at International Conference on Learning Representations (ICLR), Spotlight presentation, virtual, May 2023.
  

• Notable-top-25% (Spotlight) paper, International Conference on Learning Representations (ICLR), January 2023.

• Conference presentation: “Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks”

  - at IEEE Conference on Collaboration and Internet Computing (CIC), virtual, December 2022.
  

• Conference presentation: “Power-of-2-Arms for Bandit Learning with Switching Costs”

  - at ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Seoul, South Korea, October 2022.
  

• Invited talk: “Power-of-2-Arms for Bandit Learning with Switching Costs”

  - at the California Institute of Technology (Caltech), Pasadena, California, May 2022.
  

• Conference presentation: “Combining Regularization with Look-Ahead for Competitive Online Convex Optimization”

  - at IEEE Conference on Computer Communications (INFOCOM), virtual, May 2021.
  

• Bilsland Dissertation Fellowship, Purdue University, April 2021.

• IEEE INFOCOM Student Conference Award, US National Science Foundation (NSF), March 2021.

• Conference presentation: “On the Value of Look-Ahead in Competitive Online Convex Optimization”

  - at ACM SIGMETRICS / IFIP Performance Joint International Conference (SIGMETRICS), Phoenix, Arizona, June 2019.
  

• ACM SIGMETRICS Student Travel Grant, US National Science Foundation (NSF), May 2019.

• Conference presentation: “Competitive Online Convex Optimization with Switching Costs and Ramp Constraints”

  - at IEEE Conference on Computer Communications (INFOCOM), Honolulu, Hawaii, April 2018.
  

• IEEE INFOCOM Student Travel Grant, US National Science Foundation (NSF), March 2018.