Ming Jin

I am an assistant professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. I am also affiliated with the Power and Energy Center, Autonomy and Robotics @ VT, and the Sanghani Center for Artificial Intelligence and Data Analytics. I work on interdisciplinary problems in optimization, machine learning, control, and cyber-physical systems.

I received my PhD in Electrical Engineering and Computer Science from UC Berkeley and BEng (honors) in Electronic and Computer Engineering from the Hong Kong University of Science and Technology. I was a postdoc in Industrial Engineering and Operations Research at UC Berkeley.

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Research interests

Selected Awards

  • 1st Place Winner of the 2021 CityLearn Challenge (2021)
  • Siebel Scholar (class of 2018)
  • Best paper award, Building and Environment (2018)
  • Best paper runner-up award at EAI MobiQuitous (2016)
  • Best paper award at UBICOMM (2015)


Mar 2024

Join us at IJCAI 2024 Workshop on Trustworthy Interactive Decision Making with Foundation Models (Call for Contributions)

Jan 2024

Our project on LLMs for Supply Chain Cybersecurity, in collaboration with Prof. Peter Beling, has been selected for support by Commonwealth Cyber Initiative (CCI).

Nov 2023

Our project on safe RL for power systems, in collaboration with Prof. Javad Lavaei, has been selected for support by NSF under the Safe Learning-Enabled System Program.

Aug 2023

Our project on Safe RL for Interactive Systems with Stakeholder Alignment has been selected for support by Amazon-VT Initiative in Efficient and Robust Machine Learning.

Aug 2023

Paper on Optimization Autoformalism that uses large language models to craft optimization solutions for decision-making

Mar 2023

Paper on certified robustness for neural ODE accepted in IEEE Control Systems Letters (L-CSS)

Mar 2023

Paper on derivative-free meta blackbox (nonconvex) optimization on manifold at L4DC 2023 (oral presentation)

Mar 2023

Two papers theoretical analysis of Sobolev training and decision-focused variational inequality at IFAC World Congress 2023

Feb 2023

One paper adversarial ML (sifting out clean data from poisoned data) at USENIX Security 2023

Jan 2023

Three papers on meta-safe reinforcement learning (spotlight), model-agnostic data valuation (spotlight), and adversarial ML (certified robustness against UAP/backdoors) at ICLR 2023

Nov 2022

Three papers at AAAI on winning the CityLearn Challenge (oral), approximation/statistical properties of solution functions (oral), and nonstationary risk-sensitive RL (oral)

Jun 2022

Paper on dynamic regret for online optimization "Dynamic Regret Bounds for Online Nonconvex Optimization" to appear in IEEE Transactions on Control of Network Systems

Mar 2022

Paper on general bi-level optimization "Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints"

Mar 2022

Thanks The Commonwealth Cyber Initiative (CCI) for supporting our research

Mar 2022

Presentation at PMS 406 Autonomy MRE workshop on assured RL for dynamical systems

Feb 2022

Paper on learning under specifications "Learning Neural Networks under Input-Output Specifications" (arXiv) to appear in ACC 2022

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