Sajad Shahsavari
Doctoral Researcher
sajad.s.shahsavari@utu.fi Joukahaisenkatu 3-5 Turku Office: B6018 ORCID identifier: https://orcid.org/0000-0003-3754-1743 |
Areas of expertise
Data processing; Statistical machine learning; Deep learning; Reinforcement learning; Software engineering
Data processing; Statistical machine learning; Deep learning; Reinforcement learning; Software engineering
Research community or research topic
Machine-learning based digital twin for autonomous systems
Machine-learning based digital twin for autonomous systems
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.
Research
Publications
- A Coordinated Approach to Control Mechanical and Computing Resources in Mobile Robots (2025)
- IEEE Transactions on Robotics
(A1 Refereed original research article in a scientific journal) - A Novel Approach for Battery State-of-Health Estimation Using Convolutional Auto-Encoders (2025)
- European Control Conference
(A4 Refereed article in a conference publication ) - Co-Management of Computational and Mechanical Energy in Mobile Robots Using Reinforcement Learning (2025)
- European Control Conference
(A4 Refereed article in a conference publication ) - A Coupled Battery State-of-Charge and Voltage Model for Optimal Control Applications (2023)
- Proceedings : Design, Automation, and Test in Europe Conference and Exhibition
(A4 Refereed article in a conference publication ) - An Extension of the Kinetic Battery Model for Optimal Control Applications (2023)
- Proceedings of the IEEE International Symposium on Industrial Electronics
(A4 Refereed article in a conference publication ) - Capacity loss estimation for li-ion batteries based on a semi-empirical model (2021)
- Proceedings: European Conference for Modelling and Simulation
(A4 Refereed article in a conference publication ) - MCX - An open-source framework for digital twins (2021)
- Proceedings: European Conference for Modelling and Simulation
(A4 Refereed article in a conference publication ) - Remote Run-Time Failure Detection and Recovery Control For Quadcopters (2021)
- Journal of integrated design and process science
(A1 Refereed original research article in a scientific journal)
UTU Research Portal