Sajad
Shahsavari
Doctoral Researcher, Robotics and Autonomous Systems
Doctoral Researcher, Department of Computing
Doctoral Researcher
Machine-learning based digital twin for autonomous systems
Links
Areas of expertise
Data processing
Statistical machine learning
Deep learning
Reinforcement learning
Software engineering
Biography
Sajad Shahsavari is a Ph.D. student at University of Turku, Finland, and works as a researcher at Computational Engineering and Analysis (COMEA) research group in Turku University of Applied Sciences. His research interests include deep neural networks, time-series prediction, reinforcement learning and data analysis. He received his B.Sc. degree in Computer Engineering from Amirkabir University of Technology, Tehran, Iran in 2014 and his M.Sc. degree in Artificial Intelligence from Sharif University of Technology, Tehran, Iran in 2017.
Publications
A Novel Approach for Battery State-of-Health Estimation Using Convolutional Auto-Encoders (2025)
European Control Conference
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)
A Coordinated Approach to Control Mechanical and Computing Resources in Mobile Robots (2025)
IEEE Transactions on Robotics
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )
Simulation Of Mechanical And Computational Power Consumption In Mobile Robots (2025)
Proceedings: European Conference for Modelling and Simulation
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)
Co-Management of Computational and Mechanical Energy in Mobile Robots Using Reinforcement Learning (2025)
European Control Conference
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)
A Coupled Battery State-of-Charge and Voltage Model for Optimal Control Applications (2023)
Design, Automation, and Test in Europe Conference and Exhibition, Proceedings : Design, Automation, and Test in Europe Conference and Exhibition
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)
An Extension of the Kinetic Battery Model for Optimal Control Applications (2023)
International Symposium on Industrial Electronics (ISIE), Proceedings of the IEEE International Symposium on Industrial Electronics
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4))
Remote Run-Time Failure Detection and Recovery Control For Quadcopters (2021)
Journal of integrated design and process science
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )