Zhe Bai

Zhe (Eliza) Bai

Computational science researcher

Computing Sciences Area

zhebai@lbl.gov

Phone: +1 (510) 486-4294

1 Cyclotron Road

M/S 59R3103

Berkeley, CA 94720

Zhe Bai is a computational science researcher in the Computing Sciences Area 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 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.

Previous Appointments

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

Selected Publications

Journal Articles

Zhe Bai, Abdelilah Essiari, Talita Perciano, Kristofer E Bouchard, "AutoCT: Automated CT registration, segmentation, and quantification", Software X, January 5, 2024, 26, doi: https://doi.org/10.1016/j.softx.2024.101673

Gregory Wallace, Zhe Bai, Robbie Sadre, Talita Perciano, Nicola Bertelli, Syun'ichi Shiraiwa, Wes Bethel, John Wright, "Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive", Journal of Plasma Physics, August 18, 2022, 88:895880401, doi: 10.1017/S0022377822000708

M. Avaylon, R. Sadre, Z. Bai, T. Perciano, "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation", Advances in Artificial Intelligence and Machine Learnin, March 31, 2022, 2:288--302, doi: 10.54364/AAIML.2022.1119

Zhe Bai, Liqian Peng, "Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators", Advanced Modeling and Simulation in Engineering Sciences, 2021, 8:28, doi: 10.1186/s40323-021-00213-5

Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena, Kunihiko Taira, Steven L. Brunton, "Randomized methods to characterize large-scale vortical flow networks", PLOS ONE Journal, 2019, doi: 10.1371/journal.pone.0225265

Zhe Bai, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, Steven L. Brunton, "Dynamic mode decomposition for compressive system identification", AIAA Journal, 2019, 58:2, doi: 10.2514/1.J057870

Zhe Bai, Thakshila Wimalajeewa, Zachary Berger, Guannan Wang, Mark Glauser, Pramod K Varshney, "Low-dimensional approach for reconstruction of airfoil data via compressive sensing", AIAA Journal, April 2015, 53:4, doi: 10.2514/1.J053287

Conference Papers

GM Wallace, Z Bai, N Bertelli, EW Bethel, T Perciano, S Shiraiwa, JC Wright, "Towards Fast, Accurate Predictions of RF Simulations via Data-driven Modeling: Forward and Lateral Models", Conference, AIP Publishing, August 1, 2023, 2984, doi: https://doi.org/10.1063/5.0162422

Book Chapters

Zhe Bai, Steven L. Brunton, Bingni W. Brunton, J. Nathan Kutz, Eurika Kaiser, Andreas Spohn, Bernd R. Noack, "Data-driven methods in fluid dynamics: Sparse classification from experimental data", Book, (Springer: 2017) Pages: 323--342 doi: 10.1007/978-3-319-41217-7_17