Ming Jin

I am a Postdoctoral Scholar in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. 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. I hold a research affiliate position at the Energy Technologies Area of the Lawrence Berkeley National Laboratory (LBNL).

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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

Jan 2020

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

Aug 2019

New paper on cybersecurity for power grid "Boundary Defense against Cyber Threat for Power System Operation" (Supplementary material).

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."

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