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 and Autonomy and Robotics @ VT. I work on various interdisciplinary problems in optimization theory, learning theory, control theory, and energy 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

  • Siebel Scholar (class of 2018)
  • Best paper award, Building and Environment (2018)
  • Best paper runner-up award at MobiQuitous (2016)
  • Best paper award at Ubicomm for Mobile Ubiquitous Computing (2015)


News

Jun 2021

Thanks C3.ai Digital Transformation Institute for the grant to study RL for resilient power systems (with Prof. Alberto Sangiovanni-Vincentelli and Prof. Bo Li)

Jun 2021

New paper on data quality management "A Unified Framework for Task-Driven Data Quality Management" (arXiv)

Jun 2021

I received the SCEEE Research Initiation Grant to study safe and resilient AI for networked dynamical systems

May 2021

New paper on control by proxy "Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach" (arXiv)

May 2021

Paper on safe imitation learning "Imitation Learning with Stability and Safety Guarantees" (arXiv) to appear in IEEE Control Systems Letters

Jan 2021

Paper on online nonconvex optimization "Diminishing Regret for Online Nonconvex Optimization" to appear in 2021 American Control Conference

Jan 2021

I will teach ECE5984 Special topic: Optimization Theory for ML (spring, 2021)

Dec 2020

New paper on safe imitation learning "Imitation Learning with Stability and Safety Guarantees" (arXiv)

Dec 2020

Paper on power grid cybersecurity "Boundary Defense Against Cyber Threat for Power System State Estimation" (pdf) to appear in IEEE Transactions on Information Forensics & Security

Dec 2020

Paper on reinforcement learning "Stability-certified reinforcement learning: A control-theoretic perspective" to appear in IEEE Access

Dec 2020

Paper on robust graph convolutional network "Power up! Robust Graph Convolutional Network via Graph Powering" accepted to AAAI 2021

Sep 2020

See here a 5-minutes introduction to my research presented at the Engineering Faculty organization (EFO) faculty meeting, Virginia Tech