Michael Garay attended the University of Toledo in Ohio as a National Merit Scholar and received his B.S. degree in Physics and his B.A. degree in English Literature with Honors in 1995. He then attended the University of Arizona as a graduate student in Physics before switching to the Department of Atmospheric Science, under the supervision of Roger Davies. He earned his M.S. degree in Atmospheric Science from the University of California, Los Angeles in 2004. After moving to UCLA, he began working with the MISR team at the Jet Propulsion Laboratory as an Academic Part Time Intern in 2003. His initial work with MISR involved the development and testing of support vector machines – a type of supervised learning algorithm – for pixel classification that was ultimately infused in the MISR processing stream. He was the co-investigator on the Adaptive Sky project funded by NASA’s Earth Science and Technology Office that addressed problems of multi-instrument and multi-platform data fusion. In 2008 he received a NASA Space Act Board Award for his role in the development of the MISR INteractive eXplorer (MINX) data analysis and visualization tool. His research focuses primarily on satellite remote sensing of clouds and aerosols using MISR and other instruments. He has also done work on one- and three-dimensional radiative transfer including polarization, machine learning techniques for satellite image feature classification, computer vision approaches for image feature tracking, multi-dimensional data visualization and analysis for ground-based and satellite systems, and the sensitivity of satellite retrievals to polarimetric information.