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 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 am in the 2023-2024 academic job market for a tenure track faculty position.

Research interests

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



• Reinforcement learning (adversarial inputs, limited defender resources, safety and security, partial observability, human-in-the-loop, multi agent and scalability), bandit learning (power-of-2-arms, switching cost) and generative AI, with applications in Cloud-Edge AI, networking, wireless communications, robotics, autonomous driving, recommendation systems, smart healthcare, and NLP.
  

• Online convex optimization and online control (switching cost, prediction, uncertainty), with applications in networking, wireless communication, cloud computing, cyber-physical systems, SDN/NFV, energy and power systems, transportation, and economics.
  

• Theoretical performance analysis, e.g., competitive ratio, dynamic and static regret, sample complexity, equilibrium, convergence rate, and value of prediction.
  

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: Robotics and Smart Health”

  at Northeastern University (Northeastern), 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.