Research Interests
Exploring cutting-edge areas in AI and software engineering
Applied AI and Machine Learning
Focused on developing efficient and practical AI solutions for real-world applications
- •Temporal Action Localization in Videos
- •Efficient LLM Deployment (Pruning, Quantization)
- •Retrieval-Augmented Generation (RAG) Systems
- •Small Language Models for Edge AI
- •AI-Integrated Web Interfaces
- •Cloud-Ready ML Pipelines
Featured Projects
Showcasing innovative AI and full-stack development work
AI Doctor Assistant
FeaturedVoice-powered medical consultation platform with AI-driven healthcare assistance
Key Features:
- •LLM-based symptom analysis for natural doctor-patient conversations
- •Real-time speech understanding with VAPI Voice Assistant + OpenRouter AI
- •Automatic medical report generation (symptoms, prescriptions, advice, summary)
- and 4 more features
Technologies:
TimeClipAI: Real-Time Action Classification
FeaturedFramework for online temporal action localization using Anchor Transformers
Key Features:
- •Real-time action classification and time segmentation in videos
- •Support for EGTEA, EPIC-Kitchen 100, THUMOS'14, CricShot10 datasets
- •Pre-trained I3D features for efficient training/testing
- and 3 more features
Technologies:
DeshiPlate AI: Bangladeshi Food Recognition
FeaturedAI-powered food recognition system specialized in traditional Bangladeshi cuisine with nutrition assistance
Key Features:
- •AI-powered recognition of 33 traditional Bangladeshi dishes
- •Custom-curated dataset of Bangladeshi cuisine for model training
- •NextViT deep learning architecture achieving 89.76% accuracy
- and 5 more features
Technologies:
Recent Publications
Contributing to academic research in AI efficiency
Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
This paper introduces Z-Pruner, a novel post-training pruning technique for Large Language Models that achieves significant efficiency improvements without requiring retraining. Our method addresses the computational challenges of deploying LLMs by strategically removing redundant parameters while maintaining model performance. Extensive experiments demonstrate that Z-Pruner can reduce model size and inference time substantially while preserving the quality of generated outputs.
Technical Skills
Expertise across AI, full-stack development, and data science
Languages & Frameworks
Machine Learning & AI
Data & Visualization
Database & DevOps
Tools & Productivity
Let's Collaborate
I'm actively seeking research opportunities and collaborations in AI/ML, particularly in efficient model deployment and real-world LLM applications.