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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Applied Energy (IF: 7.9) Special Issue on energy-cyber-physical systems (submission deadline: March 2019)

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

  • 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


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."

Jan 2018

We are organizing an international workshop on "AI for energy-cyber-physical systems" as part of the International Conference on Applied Energy (ICAE 2018). See here the CFP!

Dec 2017

I have finished my PhD thesis: Data-efficient Analytics for Optimal Human-Cyber-Physical Systems.

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