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 interdisciplinary problems in optimization, learning theory, 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

  • 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)


News

Dec 2021

Paper on safe RL "Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems" (arXiv) to appear in AAAI 2022

Dec 2021

Paper on control by proxy "Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach" (arXiv) to appear in IEEE Transactions on Smart Grid

Nov 2021

Thanks 4-VA for supporting us with a collaborative research grant (with Gang Tao at University of Virginia)

Nov 2021

Two teams that I led (ROLEVT & ZoRL) jointly won the 1st place (out of 24 teams) in the CityLearn Challenge 2021. Congrats to team members: Vanshaj Khattar, Qasim Wani, Zhiyao Chang, and Mingyu Kim

Nov 2021

New paper on adversarial ML "Adversarial Unlearning of Backdoors via Implicit Hypergradient" (arXiv)

Nov 2021

Talk on assured RL for energy systems at C3.ai Digital Transformation Institute (video)

Oct 2021

My group will give an oral presentation on implicit RL in SECC 2021

Oct 2021

New paper on implicit RL "Zeroth-Order Implicit Reinforcement Learning for Sequential Decision Making in Distributed Control Systems"

Oct 2021

New paper on assured learning of neural networks "Learning Neural Networks under Input-Output Specifications"

Sep 2021

New paper on dynamic regret for online optimization "Dynamic Regret Bounds for Online Nonconvex Optimization"

Sep 2021

New paper on safe reinforcement learning "Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems" (arXiv)

Sep 2021

I will serve as an Associate Editor for IEEE Systems Journal

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)

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