Mohammed Ghaith holds a PhD from Halmstad University in Sweden and is currently a postdoc researcher at the university. His research is focused on using Evolutionary Computation (EC) methods to design and optimize Deep Learning (DL) models. Mohammed Ghaith has developed feature selection, instance selection, and hyper-parameter tuning algorithms to improve the performance of DL models. Additionally, he has explored optimizing other design decisions of DL algorithms like loss functions for survival analysis and used EC algorithms to uncover insights about complex interactions between design choices for DL models in computer vision. His work is published in top journals (Expert Systems With Applications, Information Sciences), conferences (IEEE CEC, ACM GECCO, IEEE DSAA) within the field of AI and he has received a number of recognitions (2nd place in the ESREL 2020 conference AI competition) and awards (Global Swede 2017) for research excellence
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