Prof. Dr. Samaneh Saeedinia | ICNR | Iran
Samaneh Alsadat Saeedinia is an Iranian researcher and engineer with expertise in control electrical engineering, computational neuroscience, robotics, and artificial intelligence. She holds a Ph.D. and has contributed to advanced research in system stability analysis, neural networks, and signal processing, including works on precise limit-cycle stability in epileptor models and enhanced sound event detection using spiking neural networks. Her academic footprint spans high-impact outlets such as IEEE Transactions on Systems, Man, and Cybernetics: Systems and IEEE Access. Saeedinia’s Google Scholar profile reflects a significant research impact, with 99 citations and an h-index of 6, underscoring both productivity and influence within her fields of study . She has collaborated internationally, integrating computational and control methodologies to address complex problems in neural data analysis and intelligent system design. Beyond research, Saeedinia’s work bridges theoretical innovation and practical application, contributing to interdisciplinary advances at the intersection of engineering, neuroscience, and machine learning. Her scholarly presence is further supported by her GitHub contributions, reflecting engagement with open-source research tools and reproducible science. Saeedinia continues to expand her research portfolio, fostering cross-domain collaborations and mentoring emerging scientists while advancing foundational knowledge in intelligent and adaptive systems
Featured Publications
Saeedinia, S. A., Jahed-Motlagh, M. R., Tafakhori, A., & Kasabov, N. (2021). Design of MRI-structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals. Scientific Reports, 11(1), 12064.
Saeedinia, S. A., Jahed-Motlagh, M. R., Tafakhori, A., & Kasabov, N. K. (2024). Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir spiking neural network, and NeuCube methods in a pilot study comparing epilepsy and migraine. Scientific Reports, 14(1), 10667.
Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S. A. (2024). Dynamic-structured reservoir spiking neural network in sound localization. IEEE Access, 12, 24596–24608.
Mohaghegh, M., Saeedinia, S. A., & Roozbehi, Z. (2023). Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles. Frontiers in Robotics and AI, 10, 1226028.
Saeedinia, S. A., & Tale Masouleh, M. (2022). The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision avoidance. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.