Computational Neuroimaging & Neuroscience
Exploring machine learning and computational modeling for neuroimaging and neuroscience, with an interest in understanding neural representations underlying cognition and emotion.
AI × Healthcare × Neuroimaging × Research
Research
Exploring machine learning and computational modeling for neuroimaging and neuroscience, with an interest in understanding neural representations underlying cognition and emotion.
Developing machine learning and deep learning methods for medical image analysis, clinical decision support, and explainable healthcare AI.
Graph and transformer architectures applied to biological interaction data. Evidenced by X-GNN, published at IEEE ICECTE 2026, on human protein–protein interaction prediction.
Making model decisions inspectable, which is a precondition for clinical use rather than an add-on. Evidenced by Grad-CAM in NeuroVision AI and RetinaVision AI, SHAP in the Parkinson's work, and "Explainable" in the title of the published paper.
Research methods
01Problem
Understand the problem before choosing a model
Technical stack
Projects
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Experience
Advanced Machine Intelligence Research Lab (AMIRL) (opens in a new tab)
Education
Bangladesh University of Business and Technology (BUBT)
CGPA3.82 / 4.00Begum Sheikh Fazilatun Nessa Mujib Govt. College
GPA4.00 / 5.00Gatherings


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IEEE Photonics Society Bangladesh Chapter · Sky Lounge, Mirpur-1,Dhaka,Bangladesh
A productive evening with brilliant minds at the IEEE Photonics Society Bangladesh Chapter Executive Committee Meeting. The meeting brought…
Publications
“X-GNN: An Explainable Ensemble Graph–Transformer Framework for Human Protein–Protein Interaction Prediction”, 2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 2026.
Leadership & engagement
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Recognition
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Open to research collaborations, PhD and MSc supervision enquiries, and applied AI work in healthcare.