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 (PEC). 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 in 2017 and BEng (honors) in Electronic and Computer Engineering from the Hong Kong University of Science and Technology in 2012. I was a postdoc in Industrial Engineering and Operations Research at UC Berkeley from 2018-2020.

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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

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

Sep 2020

Paper on reinforcement learning for building control to appear in ACM International Workshop on Reinforcement Learning for Energy Management (RLEM'20) "Towards Off-policy Evaluation as a Prerequisite for Real-worldReinforcement Learning in Building Control"

Sep 2020

Invited presentation at the 2020 CPES & PEC conference on "Adversarially robust learning and control for power grid"

Aug 2020

Paper on deep learning for smart inverter control to appear in IEEE SmartGridComm'20 "Deep Learning for Reactive Power Control of Smart Inverters under Communication Constraints"

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