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

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"

Aug 2020

I will teach ECE4424/CS4824: Machine learning (fall, 2020)

Aug 2020

Gokce Onen has joined my group as a PhD student.

Aug 2020

Invited presentation at the IEEE Conference on Control Technology and Applications (CCTA) on "Adversarial machine learning for energy systems"

Aug 2020

Paper on reinforcement learning for thermodynamic systems to appear in Applied Energy "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning"

Jul 2020

Thanks NSF for supporting our proposal Machine Learning for Communication-Cognizant Smart Inverter Control with Vassilis Kekatos

Apr 2020

Conversation with Berkeley IEOR (interview article)

Apr 2020

New paper on online nonconvex optimization "Diminishing Regret for Online Nonconvex Optimization"

Jan 2020

The paper "Conic Relaxations of Power System Optimization: Theory and Algorithms" to appear in European Journal of Operational Research

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