Ming Shi

Assistant Professor Department of Electrical Engineering School of Engineering and Applied Sciences Affiliated with Institute for Artificial Intelligence and Data Science University at Buffalo (The State University of New York) Email: mshi24 [AT] buffalo [DOT] edu I am actively looking for motivated Ph.D. students with strong mathematical backgrounds. Two positions (fully funded) are available until filled. Interested candidates are welcome to email your CV, transcripts, and publications (if available) to me.

Education

• Postdoc, Electrical and Computer Engineering and NSF AI-EDGE Institute, The Ohio State University, Columbus, OH, USA. Hosted by Prof. Ness Shroff and Prof. Yingbin Liang.

• Ph.D., Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN, USA. Advised by Prof. Xiaojun Lin.
  

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: “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, 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.