Publications
Research contributions
Peer-reviewed publications and preprints — with open-source code for every paper.
Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
Md. Samiul Basir Bhuiyan, Md. Sazzad Hossain Adib, Mohammed Aman Bhuiyan, Muhammad Rafsan Kabir, Moshiur Farazi, Shafin Rahman, Nabeel Mohammed
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.
Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization
Mohammed Aman Bhuiyan, Md. Sazzad Hossain Adib, Samiul Basir Bhuiyan, Amit Chakraborty, Aritra Islam Saswato, Ahmed Faizul Haque Dhrubo, Mohammad Ashrafuzzaman Khan
Fine-tunes Whisper for long-form Bangla ASR and PyAnnote's segmentation model for speaker diarization, tackling diverse acoustic conditions and speaker variability in real-world recordings. Achieves a 0.2441 Word Error Rate and a 0.2392 Diarization Error Rate on the test set, notably improving over pretrained baselines.
Reg2Bangla: An End-to-End Regional Speech Standardization
Samiul Basir Bhuiyan, Md. Sazzad Hossain Adib, Mohammed Aman Bhuiyan, Amit Chakraborty, Aritra Islam Saswato, Ahmed Faizul Haque Dhrubo, Mohammad Ashrafuzzaman Khan, Mohammad Abdul Qayum
Fine-tunes a Whisper-based ASR model on 3,350 dialectal recordings to transcribe twenty regional Bangladeshi dialects into standard Bangla text, with KenLM post-processing and KV-cache optimization for faster inference. Delivers strong performance despite substantial pronunciation and vocabulary variation across dialects.
Interests
Research interests
Current focus areas across applied AI and machine learning.
Applied AI and Machine Learning
Focused on developing efficient and practical AI solutions for real-world applications
Focus
Research focus areas
The domains where my academic work concentrates.
Efficient AI Systems
Model compression and optimization — pruning and quantization methods that preserve performance while cutting computational cost.
- Post-training pruning of Large Language Models
- Model efficiency without retraining
- Deployment optimization for edge devices
Computer Vision
Temporal action localization and video understanding through transformer-based architectures and novel training methodologies.
- Real-time action classification in videos
- Temporal segmentation with transformers
- State-of-the-art results on benchmark datasets
Get in touch
Interested in the research?
I'm actively exploring efficient AI deployment, multimodal learning, and practical LLM applications — and always open to research discussions.