Farnaz Yaghmaie

Assistant Professor at Linköping University

Sweden
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Summary

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Senior
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Top School
Farnaz Yaghmaie is an assistant professor at Linköping University specializing in reinforcement learning and control theory, with eight years of experience developing RL methods for dynamical and multi-agent systems. She earned a PhD from Nanyang Technological University (Best Thesis Award) and has held postdoctoral positions focused on adaptive-optimal control and robust RL for continuous-state, continuous-action systems. Her research blends stochastic and robust control, adaptive dynamic programming, and practical implementation on PLC-controlled industrial systems, reflecting both theoretical depth and hands-on engineering. Notably, she has worked on distinguishing dynamic versus static obstacles for SLAM and on control solutions for uncertain physical plants, demonstrating a rare bridge between academic theory and real-world deployment.
code8 years of coding experience
job6 years of employment as a software developer
bookDoctor of Philosophy - PhD, Electrical and Electronics Engineering, Best Thesis Award, Doctor of Philosophy - PhD, Electrical and Electronics Engineering, Best Thesis Award at Nanyang Technological University
bookMaster of Engineering (M.Eng.), Electrical and Electronics Engineering, A+, Master of Engineering (M.Eng.), Electrical and Electronics Engineering, A+ at Khaje Nasir Toosi University of Technology
languagesEnglish, Persian, Swedish
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Github Skills (19)

git9
github9
simulation8
simulator7
algorithms7
robotics7
api7
reinforcement-learning-environments6
reinforcement-learning6
game-development5
pybullet5
virtual-reality5
kinematics5
pytorch4
machine-learning4

Programming languages (5)

C++HTMLJupyter NotebookRich Text FormatPython

Github contributions (5)

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This is a self-contained repository to explain two basic Reinforcement (RL) algorithms.
Contributions:35 commits, 33 pushes, 1 branch in 4 months
Contributions:12 pushes, 1 branch in 1 year 2 months
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