Elphas Khata

Elphas M. Khata is a Ph.D. candidate in Imaging Science at the Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology. Originally from Kenya, he brings a multidisciplinary background in technical training, scientific programming, and applied imaging research. His doctoral research focuses on remote sensing, radiative transfer modeling, atmospheric correction, satellite data validation, and machine learning assisted environmental retrievals. In particular, his work develops physics-based and data driven algorithms for next generation Earth observation systems (Landsat 10), with emphasis on total precipitable water retrieval, land surface temperature estimation, spectral band characterization, and uncertainty quantification for satellite imaging sensors.

His broader scholarly interests include digital imaging science, hyperspectral and multispectral image analysis, sensor calibration, scalable geospatial computation, and the use of machine learning and deep learning in remote sensing workflows. Through his research, Elphas aims to advance robust Earth observation methods for environmental monitoring, climate resilience, and atmospheric surface parameter retrieval, with a long term goal of contributing to imaging science capacity development in Africa and globally.