about.

Prospective PhD candidate · applied deep learning · IEEE QPAIN 2026

S.M. Obaydur Rahman — researcher in deep learning and NLP, seeking a fully-funded PhD in hybrid deep-learning for energy, healthcare, and low-resource languages.

Trained at SUST with an undergraduate thesis on Bengali NLP — sentiment analysis of COVID-19 vaccination discourse using ensemble classical models and LSTM with Word2Vec. That line of work culminated in my IEEE QPAIN 2026 paper, "Advanced Hybrid Deep Learning Models for Solar Energy Holding Capacity Prediction in Bangladesh" — an ensemble of Random Forest, XGBoost, Gradient Boosting, AdaBoost, and a neural meta-learner reaching ~95% accuracy on Bangladeshi solar irradiance data.

My interests sit at the intersection of hybrid and ensemble deep learning, NLP for low-resource languages, AI for climate & renewable energy, and trustworthy ML for high-stakes domains. Years building production banking middleware at Mutual Trust Bank PLC ground my research in empirical rigor and real-world deployability. I welcome conversations with prospective supervisors whose agendas intersect with mine.