Abdullah

I'm a postdoctoral fellow at Hamad Bin Khalifa University in Doha, Qatar. I build machine learning methods for signals and images that come from the human body and from satellites.

Much of my published work asks a narrow question about EEG: which electrodes actually carry the information? I have answered it with graph-based channel selection, adaptive thresholds and reinforcement learning. I have also built graph neural networks for hyperscanning, where several people are recorded at once.

My current work moves in two directions. In quantum machine learning, I study when a quantum layer earns its place inside a hybrid model, and I use the geometry of the circuit's tangent space to explain barren plateaus. In medical imaging, I review how deep learning corrects motion in PET scans and how well those methods are validated.

You can also look at

Quantum Computing The main ideas of quantum computing, one topic at a time, and how they connect to each other.

Featured Research Publications

HyperGCN

Interpreting hyperscanning EEG signals for common multi-task classification using graph convolutional networks

IEEE Signal Processing Letters 2025

G-EEGCS

Graph-based optimum electroencephalogram channel selection

Biomedical Signal Processing and Control 2024

k-AdaptEEGCS

Adaptive threshold based automatic EEG channel selection

IEEE Sensors Letters 2024


Updates!