Research
Our research spans four core areas of artificial intelligence, each driven by the goal of creating impactful, ethically sound solutions.
Medical Imaging
Developing AI-driven diagnostic tools for brain tumors, skin cancer, breast ultrasound, ocular diseases, and fungal classification using self-supervised learning, federated learning, and advanced CNN architectures.
Computer Vision
Building robust visual recognition systems that push the boundaries of image classification and segmentation for scientific and medical applications.
Explainable AI
Making AI decisions transparent and interpretable. Our work focuses on integrating explainability into federated learning and diagnostic systems to ensure trust in clinical settings.
NLP
Advancing natural language processing with transformers for Bengali text summarization, ethical content classification, and child speech recognition systems.
Programs
How you can get involved with the lab.
Research Partnerships
Collaborate with faculty and global teams on multi-disciplinary AI projects focused on medical, science, and social impact.
Student Internships
Open to motivated students seeking hands-on AI research experience, mentorship, and project ownership.
Open Communication
The lab encourages idea sharing, inclusive discussion, and a culture where curiosity drives innovation.