Ming Shi

Assistant Professor, Department of Electrical Engineering (UB Homepage) Affiliated with Institute for Artificial Intelligence and Data Science University at Buffalo (SUNY Buffalo) Faculty Collaborator, Department of Computer Secience and Engineering, The Ohio State University Email: mshi24 [AT] buffalo [DOT] edu (preferred), shi.1796 [AT] osu [DOT] edu Office: 228 Davis Hall Dr. Shi received his Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA, in 2022. His Ph.D. advisor is Prof. Xiaojun Lin. From 2022 to 2024, he worked as a Post-Doctoral Scholar at the Department of Electrical and Computer Engineering of The Ohio State University, Columbus, OH, USA, and was affiliated with the NSF AI-EDGE Institute, hosted by Prof. Ness Shroff and Prof. Yingbin Liang.

Research interests

Dr. Shi's research strives to develop AI-powered autonomous networked systems with strong theoretical performance guarantees under dynamics, uncertainty, and practical domain-specific constraints, with a focus on:



• Reinforcement Learning, Bandit Learning, and LLMs

• Online (Non-)Convex Optimization and Online Control

• Networking, Wireless Communication, and Edge-AI

• Autonomous Vehicles, Robotics, and Recommendation Systems

• Power Systems, Data Security, and Social Networks

News and Honors

• Conference presentation: “Designing Near-Optimal Partially observable Reinforcement Learning”

  - at IEEE Military Communications Conference (MILCOM), Washington, DC, October 2024.
  

• Invited talk: “RL for Networking: Safety, Adversarial Inputs, Partial Observability, and Human Feedback"

  - at Arizona State University (ASU), Tempe, Arizona, May 2024.
  

• 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, May 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.