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.

  ·     ·     ·     ·  


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

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)

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)