Research
My research interests center on efficient, trustworthy, and multimodal learning systems, with a current focus on medical AI and lightweight vision-language models.
Research Interests
- Efficient AI: model compression, efficient inference, lightweight architectures
- Trustworthy AI: robustness, fairness, interpretability
- Theoretical ML: information theory, learning theory
- Multimodal Learning: vision-language models, medical AI
- Reinforcement Learning: decision-making, sequential learning
Undergraduate Thesis
Development of a Multimodal Medical Assistance Chatbot for Domain-Specific Applications
Supervised by Prof. Mohammad Ariful Haque, with collaborators Tanvir Ahmed Khan and Ismam Nur Swapnil.
Developed a dermatology-focused multimodal medical assistant by fine-tuning a vision-language model on DermNet, using GRPO and DPO for reasoning and conversational alignment, integrating DINOv2 and knowledge-graph-based RAG for diagnostic precision, and applying structured pruning for efficient deployment.
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