His team conducted a VIS/NIR LiDAR airborne data collection and built a well-ground truthed collection. He has engaged in a variety of research activities in unmixing (an inverse-problem), dimensionality reduction, and classification using NASA and NSF big data sets and investigated hyperspectral and color imaging for disease detection in apples and for detecting and estimating ripeness of blueberries.
He is developing physics-based, data-driven nonlinear, and hybrid computational models for the inverse problem of unmixing.
In June 2013, he was General Chair of the IEEE Workshop on Hyperspectral Image and Signal Processing (WHISPERS). Professor Gader gave tutorials on sub-pixel analysis (unmixing) at WHISPERS in 2016 and 2018. He is currently writing a textbook on this topic.
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Current research includes studying the unstable behavior of deep networks when presented with samples from unknown distributions.
Paul Gader has published over 100 journal and over 300 total papers, served for three years as a UF Research Foundation Professor and was named an IEEE Fellow in 2011.