Mohammad Ghaith Altarabichi

Mohammad Ghaith Altarabichi is a postdoctoral researcher at the School of Information Technology, Halmstad University. His research focuses on leveraging Evolutionary Computation (EC) to optimize Deep Learning (DL) models, covering feature selection, hyperparameter tuning, and architectural decisions like loss and activation functions. He has published in top-tier venues such as Expert Systems With Applications, Information Sciences, and conferences like GECCO and IEEE CEC.

His work intersects Artificial Intelligence, Battery Technology, and Transportation Systems, particularly in optimizing DL for battery state-of-health estimation and fault detection in evolving environments. Recent publications explore Randomness Techniques in DNNs and Kolmogorov-Arnold Networks (KANs) for regularization and performance enhancement.

Scientific Awards:

• 2nd place in ESREL 2020 AI competition
• Global Swede 2017 award

He has contributed to advancing Evolutionary Deep Learning frameworks, with applications spanning Health Informatics, Computational Neuroscience, and Energy Systems. His research also includes Accessible Technology, such as a vision-based indoor navigation system for visually impaired individuals.