Oluwafemi O. Omitusa

I’m a second-year PhD student in Atmospheric Sciences at Stony Brook University, advised by Prof. Pavlos Kollias. Prior to joining Stony Brook, I earned my M.S. in Meteorology at the University of Oklahoma, Norman, where I was advised by Dr. david Bodine.

My research interests include remote sensing of severe convective storms, machine and deep learning, and GIS. My current research focuses on developing intelligent, data-driven algorithms that optimize the adaptive sampling of severe weather phenomena (e.g. deciding what to measure, when, and where) within the Multisensor Agile Adaptive Sampling (MAAS) framework, with the goal of enhancing data quality and supporting more accurate forecasting.

At the core of my philosophy is a belief in simplicity and practical impact. I like approaches that make sense of severe weather without overcomplicating things, while still capturing its inherent complexity and improving forecasting. As Einstein said, “Make things as simple as possible, but not simpler.” This philosophy guides how I design data-driven methods that are both effective and scalable.