Amirhossein Taghvaei

Assistant Professor · Dept. of Aeronautics & Astronautics · University of Washington Seattle

I am an Assistant Professor in the William E. Boeing Department of Aeronautics & Astronautics at the University of Washington (UW), Seattle. I lead a research group at the intersection of control theory, statistical inference, and machine learning, focused on principled and scalable decision-making under uncertainty. Our work is motivated by applications in aerospace systems, robotics, and environmental monitoring, with the broader goal of developing a unified framework for the control of uncertainty in complex dynamical systems.


Before joining UW, I completed my Ph.D. at the University of Illinois at Urbana-Champaign and was a postdoctoral scholar at the University of California, Irvine, with Prof. Tryphon Georgiou. Check out my Google Scholar for an up-to-date list of publications. I am always looking for motivated Ph.D. students — please reach out if our interests align.

Funding NSF-supported research through awards EPCN-2318977 and EPCN-2347358 on scalable nonlinear filtering and stochastic control.
Paper 9 papers in leading machine learning venues (ICML, NeurIPS, L4DC) and 26 journal articles in top control and applied mathematics journals.
Talk Semi-plenary lecture at MTNS 2024, Cambridge, UK.
Student Mohammad Al-Jarrah received the CDC 2024 Outstanding Student Paper Award and did a summer internship with the Morgan Stanley’s Machine Learning team.

Research

Our research vision is to develop a unified mathematical and computational framework that closes the loop between models, data, and decisions, enabling autonomous systems to reason, sense, and act reliably in uncertain and dynamic environments. Our work is organized around two complementary directions: (i) scalable and principled methods for nonlinear filtering, enabling accurate inference in high-dimensional, nonlinear, and non-Gaussian settings using geometric and variational tools such as optimal transport; and (ii) the interplay of stochastic control and generative modeling, leveraging time-reversal and diffusion-based methods to design control strategies that shape uncertainty at the distributional level. Together, these efforts integrate estimation and control into a coherent framework for decision-making under uncertainty, with the potential to enable safer, more efficient, and more sustainable autonomous systems.


Our research is supported by the National Science Foundation (NSF) grants EPCN-2318977 and EPCN-2347358.

Selected Recent Publications

2026
Yuhang Mei, Amirhossein Taghvaei.
Submitted to the IEEE Control Systems Letters
2025
Yuhang Mei, Amirhossein Taghvaei, Ali Pakniyat.
IEEE Conference on Decision and Control (CDC), December, 2025
2025
Mohammad Al-Jarrah, Bamdad Hosseini, Niyizhen Jin, Michele Martino, Amirhossein Taghvaei.
Submitted to the SIAM Journal on Uncertainty Quantification
2025
Yuhang Mei, Mohammad Al-Jarrah, Amirhossein Taghvaei, Yongxin Chen.
Annual Learning for Dynamics \& Control Conference (L4DC), PMLR 283:484-496, 2025
2025
Bamdad Hosseini, Alexander W Hsu, Amirhossein Taghvaei.
SIAM/ASA Journal on Uncertainty Quantification, 13(1), pp.304-338, 2025
2024
Mohammad Al-Jarrah, Bamdad Hosseini, Amirhossein Taghvaei.
IEEE Conference on Decision and Control (CDC), December 2024
Outstanding Student Paper Award
2024
Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei.
International Conference on Machine Learning (ICML), PMLR 235:813-839, 2024.

Teaching

Estimation and System Identification
AA/EE/ME 549 · Graduate
Spring 2023, 2025, 2026
Linear System Theory
AA/EE 547 · Graduate
Winter 2024, 2025, 2026
Nonlinear Control Systems
AA/EE/ME 583 · Graduate
Fall 2021, 2022, 2023, 2025
Control In Aerospace Systems
AA 447 · Undergraduate
Spring 2022, 2024

Group Highlights

Feb 2026
Paper Our article on A Lasso‐Alternative to Dijkstra's Algorithm for Identifying Short Paths in Networks is now published. This is a joint work with Anqi Dong and Tryphon Georgiou.
Dec 2025
Talk We are organizing a three part invited session on "Optimal transportation methods for estimation and control" at the IEEE Conference on Decision and Control (CDC), Brazil, 2025.
Oct 2025
Talk I presented our research on From diffusion models to stochastic control: a time-reversal methodology for feedback control design at the Applied Mathematics Seminar Series, University of Washington, Seattle
July 2025
Paper We have three accepted papers A Time-Reversal Control Synthesis for Steering the State of Stochastic Systems, Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps, and Error Analysis of Sampling Algorithms for Approximating Stochastic Optimal Control, for presentation and publication in the proceedings of the at the IEEE Conference on Decision and Control (CDC).
June 2025
Student Mohammad Al-Jarrah is presenting his research at the at Morgan Stanley’s Machine Learning Research Talks, where he will join as intern during summer.
Mar 2025
Award Our NSF supported research "Fundamentals of Power Generation from Thermal Anisotropy - A Stochastic Control Framework" is featured on the University of Washington newsletter .
Feb 2025
Paper Our articles on “Flow matching for stochastic linear control systems” and "Interacting Particle Systems for Fast Linear Quadratic RL" are accepted for presentation at the 7th Annual Learning for Dynamics & Control (L4DC) Conference, Ann-Arbor, Michigan.
Dec 2024
Student Mohammad Al-Jarrah wins the outstanding student paper award for his work on "Data-Driven Approximation of Stationary Nonlinear Filters with Optimal Transport Maps" at the IEEE Conference on Decision and Control, Milan, Italy.
Aug 2024
Talk I delivered a semi-plenary lecture, titled “Towards data-driven nonlinear filtering algorithms”, at the 26th International Symposium on Mathematical Theory of Networks and Systems (MTNS), Cambridge, UK.
Aug 2024
Paper Our article "Minimal entropy production in the presence of anisotropic temperature fields" is accpeted to be published at the IEEE Transactions of Automatic Control (TAC).
June 2024
Student Ph.D. student, Mohammad Al-Jarrah, wins the Doctoral Research Excellence award from the Department of Aeronautics & Astronautics, University of Washington, Seattle.
June 2024
Award Our collaborative research proposal “Fundamentals of Power Generation from Thermal Anisotropy - A Stochastic Control Framework”, with Prof. Tryphon Georgiou, is awarded by the Energy, Power, Control, and Networks (EPCN) program at National Science Foundation (NSF).
May 2024
Paper Our article “Nonlinear Filtering with Brenier Optimal Transport Maps” is accepted to be presented at the International Conference of Machine Learning (ICML), Vienna, July, 2024