Biography

Amir Taghvaei is an Assistant Professor in the William E. Boeing department of Aeronautics and Astronautics at the University of Washington (UW) Seattle. He obtained his Ph.D. degree from University of Illinois at Urbana-Champaign where he was a member of Decision and Control Lab (DCL) in the Coordinated Science Laboratory (CSL). He was a Postdoctoral Scholar with Tryphon Georgiou at  University of California, Irvine before joining UW.  His research interests lie at the intersection of control theory, machine learning, and physics, with the aim of designing scalable and reliable computational algorithms and understanding fundamental limitations.   

Education

• Ph.D. in Mechanical Engineering, University of Illinois at Urbana-Champaign, 2019
• M.S. in Mathematics, University of Illinois at Urbana-Champaign, 2017
• B.S. in Mechanical Engineering, Sharif University of Technology, Iran, 2013
• B.S. in Physics, Sharif University of Technology, Iran, 2013

Previous appointments

• Postdoctoral Scholar, Department of Mechanical and Aerospace Engineering, University of California, Irvine, 2019

Select publications

1. A. Taghvaei, P. G. Mehta. Optimal Transportation Methods in Nonlinear Filtering: The feedback particle filter. IEEE Control Systems Magazine (CSM), 2021
2. A. Taghvaei, P. G. Mehta. An optimal transport formulation of the ensemble Kalman filter. IEEE Transactions of Automatic Control (TAC), vol. 66, no. 7, pp. 3052-3067, July 2021.
3. A. Taghvaei, P. G. Mehta, S. P. Meyn. Diffusion map-based algorithm for gain function approximation in the feedback particle filter. SIAM/ASA Journal on Uncertainty Quantification, 8(3):1090–1117, 2020
4. A. Taghvaei, O. Movilla Miangolarra, R.i Fu, Y. Chen, T. T. Georgiou On the relation between information and power in stochastic thermodynamic engines IEEE Control Systems Letters (L-CSS), 2021.
5. R. Fu, A. Taghvaei, Y. Chen, T. T. Georgiou. Maximal power output of a stochastic thermodynamic engine. Automatica, 123:109366, 2021.
6. A. Taghvaei, T. T. Georgiou, L. Norton, A. R. Tannenbaum. Fractional SIR Epidemiological Models. Scientific Reports, 10(1):20882, 2020.
7. J. Fan, A. Taghvaei, Y. Chen Scalable computations of Wasserstein barycenter via input convex neural networks International Conference of Machine Learning (ICML), 2021.
8. A. Taghvaei, A Makkuva, S. Oh, J. Lee. Optimal transport mapping via input-convex neural networks. International Conference on Machine Learning (ICML), 6672-6681, June 2020.
9. A. Taghvaei, P. G. Mehta, Accelerated flow for probability distributions. International Conference on Machine Learning (ICML), 6076–6085, Long Beach, June 2019.