Research Assistant | AMIR Lab | Bangladesh
Md. Shakil Hossain is a Research Assistant at AMIR Lab with expertise in artificial intelligence, humanoid robotics, and data-driven solutions. He earned a B.Sc. in Computer Science from Bangladesh University of Business and Technology, where he specialized in artificial intelligence, machine learning, neural networks, IoT, and data science. Professionally, he has served as an Assistant Robotics Engineer at Robo Tech Valley, where he led the development of educational and multipurpose humanoid robots, and as an AI Data Trainer at Invisible Technologies, contributing to high-quality datasets for machine learning systems. His current research focuses on natural language processing, hybrid deep learning models, multimodal AI, and large language model applications, with several high-impact publications in Scientific Reports, IEEE Access, Knowledge-Based Systems, and arXiv. His notable works include the Multi-task Opinion-Enhanced Hybrid BERT model for mental health analysis, hybrid transformer-based models for Arabic text classification, and novel graph-based approaches for aspect-based sentiment analysis. He has also contributed to IoT-based agricultural solutions and real-time AI model deployment. Recognized for his excellence, he has led champion teams in multiple hackathons, including the BCS ICT Fest and Cisco IoT Hackathon, and received a Research & Development Grant from BUBT for his IoT-based Smart Agro-Monitor project. He holds multiple global certifications in data analytics, computer vision, and responsible AI, and has actively organized robotics Olympiads. Md. Shakil Hossain’s combined technical expertise, impactful research, and leadership in innovation make him a strong candidate for this award.
Profile: Google Scholar | Scopus | ORCID
Featured Publications
MM Hossain*, MS Hossain, MF Mridha, M Safran, S Alfarhood, Multi-task opinion-enhanced hybrid BERT model for mental health analysis. Sci. Rep., 2025, 15(1), 3332.
MM Hossain*, MS Hossain, M Safran, S Alfarhood, M Alfarhood, A hybrid attention-based transformer model for Arabic news classification using text embedding and deep learning. IEEE Access, 2024.
MM Hossain*, MS Hossain, MS Hossain, MF Mridha, M Safran, TransNet: deep attentional hybrid transformer for Arabic posts classification. IEEE Access, 2024.
MM Hossain*, MS Hossain, S Chaki, MR Hossain, MS Rahman, ABM Ali, CrosGrpsABS: Cross-attention over syntactic and semantic graphs for aspect-based sentiment analysis in a low-resource language. arXiv Preprint, 2025, arXiv:2505.19018.
MS Hossain*, MM Hossain, MS Hossain, MF Mridha, M Safran, EmoNet: Deep attentional recurrent CNN for X (formerly Twitter) emotion classification. IEEE Access, 2025.
