معرفی
Farnaz Adib Yaghmaie serves as an Assistant Professor and Docent at Linköping University's Department of Electrical Engineering (ISY), specializing in the Automatic Control division. Her research bridges control theory and machine learning, with significant contributions to reinforcement learning applications in complex systems.
Education:
- Ph.D. in Electronic and Electrical Engineering, Nanyang Technological University (NTU), Singapore (2017, Best Thesis Award)
- Master's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2011)
- Bachelor's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2009)
Her research focuses on redefining machine learning paradigms for control problems, particularly through reinforcement learning applications. Current projects include Foundation Models and RL for General-Purpose Control (2025-present), Online Control with Adversarial Noise (2022-present), and RL for Partially Observable Systems (2021-present). She explores how foundation models can enable control systems to generalize across diverse tasks and adapt to unseen scenarios, with applications in robotics and autonomous systems. Her work integrates large language models and generative AI into control frameworks, addressing fundamental challenges in sequential decision-making under uncertainty.
Scientific Recognition:
- Best Thesis Award at Nanyang Technological University (2017)
- CENIIT research grant (2020)
- Co-founded SEDDIT competence centre (2024)
As an educator, she teaches Ph.D.-level courses including Reinforcement Learning (2021-2024) and Advanced Robotics (2025), with her WASP course ranking as the second-best Ph.D. course in the program (3.9/5 evaluation). She actively mentors Ph.D. students including Abbas Pasdar, who is working on Foundation Models for General-Purpose Control. Her research is supported by grants including CENIIT and collaborations through the SEDDIT competence centre. She contributes to the academic community through workshops like the LINK-SIC Core Competence Workshops on Reinforcement Learning and by chairing sessions at major conferences including ECC 2025.
