Zhe Bai is a postdoctoral scholar in the Machine Learning & Analytics Group in the Scientific Data 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.
- Ph.D., Mechanical Engineering, University of Washington, 2018.
- 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.
- Research Assistant, University of Washington, Seattle, WA.
- Research Intern, Sandia National Laboratories, Livermore, CA.
- Research Assistant, Syracuse University, Syracuse, NY.
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. [https://doi.org/10.2514/1.J057870]
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]