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Zhe Bai

zhe
Zhe (Eliza) Bai
Postdoctoral Scholar
Computational Research Division
Phone: +1 (510) 486-4294
1 Cyclotron Road
M/S 59R3103
Berkeley, CA 94720

Zhe Bai is a postdoctoral scholar in the Data Analytics & Visualization Group in the Computational Research Division at Lawrence Berkeley National Laboratory. Her research interests lie in the area of sparse sampling and model order reduction, including compressed sensing, machine learning and large-scale computation and simulation. Cultivated interdisciplinary research and collaborations spanning the fields of engineering and applied mathematics, her work involves data-driven modeling that leverages advanced data science techniques to understand, estimate and control high-dimensional physical systems.


Education

  • Ph.D., Mechanical Engineering, University of Washington2018.
  • M.S., Applied Mathematics, University of Washington, 2017.
  • M.S., Mechanical & Aerospace Engineering, Syracuse University, 2013.
  • B.S. Thermal Energy & Power Engineering, Harbin Institute of Technology, 2011.

Previous Appointments

  • Research Assistant, University of Washington, Seattle, WA.
  • Research Intern, Sandia National Laboratories, Livermore, CA.
  • Research Assistant, Syracuse University, Syracuse, NY.

Selected Publications

1. Z. Bai, E. Kaiser, J. Proctor, J. Kutz and S. Brunton, "Dynamic mode decomposition for compressive system identification", Invited submission to AIAA Journal - Special invited section "Modal Analysis for Fluid Flows: Applications and Outlook", 2017. [arXiv:1710.07737]

2. Z. Bai, S. Brunton, B. Brunton, J. Kutz, E. Kaiser, A. Spohn and B. Noack, "Data-driven methods in fluid dynamics: sparse classification from experimental data", Whither Turbulence and Big Data in the 21st Century, Springer, pp. 323-342, 2016. [https://doi.org/10.1007/978-3-319-41217-7_17]

3. Z. Bai, T. Wimalajeewa, Z. Berger, M. Glauser and P. Varshney, "Low-dimensional approach for reconstruction of airfoil data via compressive sensing", AIAA Journal, Vol. 53, No. 4, pp. 920-933, 2015. [https://doi.org/10.2514/1.J053287]