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tanbui@oden.utexas.edu
512-471-8176
Office Location: POB 3.118, ASE 4.220
Tan Bui-Thanh
Professor
William J. Murray, Jr. Fellowship in Engineering No. 4
Department Research Areas:
Computational Engineering
Education:
Ph.D., Massachusetts Institute of Technology
Research Interests:
- Quantum-accelerated SciML algorithms for digital twins
- Model-constrained Scientific Deep Learning (SciDL) algorithms
- Inverse problems
- Uncertainty quantification
- Numerical analysis, numerical optimization, and reduced-order modeling (model order reduction)
Dr. Tan Bui-Thanh is a professor and the endowed William J Murray Jr. Fellow in Engineering No. 4 in the Department of Aerospace Engineering and Engineering Mechanics (ASE/EM) and the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. Bui-Thanh obtained his Ph.D. from the Massachusetts Institute of Technology in 2007. In 2013, he joined the ASE/EM department in the Cockrell School of Engineering as an assistant professor.
He has decades of experience and expertise in multidisciplinary research across the boundaries of different branches of computational science, engineering and mathematics. Bui-Thanh is currently a co-director of the Center for Scientific Machine Learning at the Oden Institute. He is a former elected vice president of the SIAM Texas-Louisiana Section and a former elected secretary of the SIAM SIAG/CSE. Bui-Thanh received the NSF (OAC/DMS) early CAREER award, a recipient of the Oden Institute distinguished research award, a two-time winner of the Moncrief Grand Challenge award, and a Gordon Bell Prize finalist.
Bui-Thanh has extensive expertise with a track record on PDE-constrained inverse problems, Bayesian inverse problems, uncertainty quantification (UQ), reduced-order modeling (ROM) and surrogate, and high-order finite element methods. He has developed various physics-aware SciML approaches for computational sciences, engineering and mathematics. In particular, he has published many works on real-time forecast and calibration (inversion) SciML algorithms that could be deployed for digital twin applications. He is currently working on Quantum-accelerated SciML algorithms for digital twins.
