After KAUST Academy
Abdullah published a paper on transfer learning in P300-based brain-computer interfaces at the AAAI Conference on Artificial Intelligence in April 2025 and earned two NVIDIA generative AI associate certifications in July 2025. As of October 2026, Abdullah works at Aramco.
Summer project
Bioinformatics Specialization · Cohort 2024
Exploring Novel Machine Learning Techniques for Brain Computer Interface (BCI) Applications
This study explores the effectiveness of transfer learning and domain adaptation in brain-computer interfaces (BCI). Individuals suffering from severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS), rely on BCI technologies to communicate. We compared the performance of machine learning models trained with ALS-specific data versus non-ALS data, focusing on character accuracy and estimated stopping time. By employing EEGNet and Stepwise Linear Discriminant Analysis (SWLDA), we determined the benefit of employing non-ALS data for pre-training and fine-tuning machine learning models. Our findings suggest potential improvements in accuracy for individuals with ALS through the use of non-ALS data.
With KAUST Academy
- Cohort 2024
Duke University · Bioinformatics Summer Internship
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