Amir Taghvaei

Amir Taghvaei is an Assistant Professor in the William E. Boeing Department of Aeronautics and Astronautics at the University of Washington, Seattle. His research focuses on control theory, machine learning, stochastic thermodynamics, and optimal transportation. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and completed a postdoctoral fellowship at the University of California, Irvine.

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 and Physics, Sharif University of Technology, Iran, 2013

Research Interests:

• Design of scalable algorithms for nonlinear filtering and estimation
• Intersections of control theory, machine learning, and physics
• Stochastic thermodynamics and energy harvesting from thermal anisotropy
• Optimal transport theory applications in control and machine learning

Recent Trends in Publications: His work emphasizes data-driven algorithms, optimal transport formulations for filtering, and stochastic control systems. Recent contributions include advancements in nonlinear filtering using Brenier maps and entropy production analysis in thermodynamic engines.

Grants & Collaborations:

• Funded by NSF for projects on optimal transport methods and thermal anisotropy
• Collaborations with researchers like Tryphon Georgiou, Yongxin Chen, and Bamdad Hosseini

Labs & Groups: Leads research in variational optimal transport methods and stochastic control within the University of Washington’s Aeronautics & Astronautics department.