Ming Jin

I am a Postdoctoral Scholar in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley mentored by Prof. Javad Lavaei. I graduated rom the Department of Electrical Engineering and Computer Sciences at UC Berkeley advised by Prof. Costas Spanos.

I hold a research affiliate position at the Energy Technologies Area of the Lawrence Berkeley National Laboratory (LBNL), working with Dr. Wei Feng and Dr. Chris Marnay.

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Our Berkeley team is participating in the ARPA-E $4M Cash Prize competition on grid optimization (link 1, link 2)

My research focuses on learning, control, and optimization methods with applications to cyber-physical human systems such as smart buildings, smart grids, and urban systems. See see my publications.

Selected Awards

  • Siebel Scholar (class of 2018)
  • Best paper award, Building and Environment (2018)
  • Awards of Student Technology Fund Initiative (2016,2017)
  • Best paper award at MobiQuitous (2016)
  • Finalist for Samsung Fellowship Award (2016)
  • Best paper award at Ubicomm for Mobile Ubiquitous Computing (2015)
  • Top 5 team in Microsoft Indoor Localization Competition, Seattle (2015)
  • Academic excellence award, first class honors, Dean’s List, 2008-2012
  • ECE Department Scholarship, 2008-2012
  • School of Engineering Scholarship, 2008-2012
  • University Scholarship, 2008-2012

News

Jul 2019

Paper to appear in Proc. 58th IEEE Conference on Decision and Control "Towards Robust and Scalable Power System State Estimation".

May 2019

New paper on robust graph convolutional network "Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering".

Feb 2019

New paper on power system state estimation "Scalable and robust state estimation from abundant but untrusted data".

Oct 2018

New paper on stability certfied reinforcement learning "Stability-certified reinforcement learning: a control-theoretic perspective".

Aug 2018

Presentations at the International Conference on Applied Energy: "BISCUIT: Building Intelligent System Customer Investment Tools" (paper | slides) and "Advanced Building Control via Deep Reinforcement Learning" (paper).

Jul 2018

Presentation at the 23rd International Symposium on Mathematical Programming, Bordeaux, France: "Vulnerability analysis and robustification of power grid state estimation" (slides).

Jun 2018

New paper on power system vulnerability analysis "Power Grid AC-based State Estimation: Vulnerability Analysis Against Cyber Attacks" accepted in IEEE Transactions on Automatic Control.

Mar 2018

New paper to guarantee safety of smooth RL in real-world systems like power grids: "Control-theoretic analysis of smoothness for stability-certified reinforcement learning."

Mar 2018

New paper on observer design for large-scale nonlinear systems: "Multiplier-based observer design for large-scale Lipschitz systems."

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