Nazmus Sayadat Rumman is an Applied Mathematician and aspiring AI researcher with a genuine passion for understanding how intelligent systems work — not just using them, but building and questioning them from the ground up. He completed his M.Sc. in Applied Statistics and Data Science from Jahangirnagar University and his B.Sc. in Applied Mathematics from the University of Rajshahi, where he developed the quantitative intuition that continues to shape how he approaches problems.
Over the years, Rumman has worked across the ML stack — from data preprocessing and statistical modeling to deep learning implementation — with hands-on experience in Python, PyTorch, TensorFlow, and Scikit-learn. His projects include a brain tumor MRI segmentation system and a heart disease prediction model integrated with a real-time FastAPI deployment, both reflecting his interest in building things that actually work, not just things that look good on paper. He holds certifications from Stanford University, DeepLearning.AI, IBM, and Google, though he is the first to acknowledge that certificates are starting points, not destinations.
Outside of technical work, he has led teams, coordinated community initiatives, and learned firsthand that good research — like good teamwork — depends as much on clear communication and intellectual honesty as it does on technical skill. He brings that same mindset into research settings: curious, collaborative, and willing to say when he does not know something.
Rumman is currently focused on deepening his expertise in parameter-efficient fine-tuning and reservoir computing, with the long-term goal of contributing to ML research through a PhD. He is drawn to problems that sit at the boundary of mathematical theory and practical AI systems — the kind of problems where the right question matters more than a fast answer.
