Yifan Zhou (周一凡)

Yifan Zhou (周一凡)

Assistant Professor

Stony Brook University

Biography

Yifan Zhou is an assistant professor at Department of Electrical and Computer Engineering, Stony Brook University. She has been a postdoctoral researcher at Stony Brook University, working with Professor Peng Zhang. She received her Ph.D. from the Department of Electrical Engineering at Tsinghua University in July 2019, under the supervision of Professor Yong Min. Before that, she received her B. Sc. in Electrical Engineering in 2014 from the Department of Electrical Engineering at Tsinghua University.

I am looking for self-motivated Ph.D. students with a solid background in power system static/dynamic analysis and with strong interets in machine learning or quantum computing. Please send me an email with your CV if you are interested.

Interests
  • Networked Microgrids
  • Formal Analysis
  • AI-Driven Smart Grid
  • Quantum Computing
Education
  • Ph.D. in Electrical Engineering, 2019

    Tsinghua University

  • B.E. in Electrical Engineering, 2014

    Tsinghua University

News

📢 2022/06 Our paper APF is accepted by IEEE Transactions on Power Systems.
📢 2022/05 Our paper QSLR is accepted by IEEE Transactions on Power Systems.
📢 2022/05 Our paper NISQ-QEMTP is accepted by IEEE Transactions on Power Systems.
📢 2022/05 Our paper QTSA is accepted by IEEE Transactions on Power Systems.
📢 2022/02 Our paper NeuEMTP is accepted by PESGM 2022.

Projects

• Solar PLUS: Solar Integration through Physics-Aware Learning Based Ultra-Scalable Modeling and Analytics. DOE. Co-PI. PI: Peng Zhang.
• University – Navy Research Collaboration on Robust Energy Infrastructure and Resiliency. ONR. Co-PI. PI: Yacov Shamash.
• Dynamic Equivalencing of Power Systems. ISO New England. Co-PI. PI: Peng Zhang.

Presentations

Learning-Based, Verifiable Smart Grids
This talk summarizes my recent research on learning-based, verifiable smart grids which collaboratively integrates machine learning, formal methods, and quantum computing to enable resilient power system operations with high levels of renewable energy penetration.
Learning-Based, Verifiable Smart Grids

Publications

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(2022). Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems. Accepted by IEEE Transactions on Power Systems.

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(2022). Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program. Accepted by IEEE Transactions on Power Systems.

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(2021). Neuro-Reachability of Networked Microgrids. IEEE Transactions on Power Systems, accepted.

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(2021). Reachable Dynamics of Networked Microgrids with Large Disturbances. IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2416-2427.

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(2021). Reachable Power Flow: Theory to Practice. IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2532-2541.

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(2021). Quantum Electromagnetic Transients Program. IEEE Transactions on Power Systems, accepted.

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(2020). Reachable Eigenanalysis. IEEE Transactions on Power Systems, vol. 35, no. 6, pp. 4936-4939.

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(2020). An ODE-Enabled Distributed Transient Stability Analysis for Networked Microgrids. 2020 IEEE Power & Energy Society General Meeting.

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(2020). Reachable Power Flow. IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 3290-3293.

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Awards

🏆 Outstanding Reviewer for IEEE Transactions on Power Systems, 2020
🏆 Outstanding Graduate of Tsinghua University (1.5%), 2014
🏆 Outstanding Graduate of Beijing, 2014
🏆 Outstanding Thesis Award, Tsinghua University, 2014