Philip Brodrick
Dr. Philip Brodrick is a data scientist, software systems engineer, and research technologist at NASA’s Jet Propulsion Laboratory in Pasadena, California. Phil focuses on utilizing imaging spectroscopy to understand environmental interactions, ranging from predicting drought impacts on vegetation to quantifying the impact of surface mineralogy on global radiative forcing. In this inherently data rich field, Phil emphasizes embedding data science solutions into mission designs. He is on the Science Team for the Earth Mineral Dust Source Investigation (EMIT), and supports Pre-Phase A activities for the Surface Biology and Geology (SBG) mission. He is also active with a variety of airborne spectrometer programs, including AVIRIS, AVIRIS-NG, and NEON.
Prior to his work at JPL, Phil worked with the Carnegie / Global Airborne Observatory on ecological and conservation projects around the globe. Phil came to remote sensing after research in multiple parts of the energy sector. Phil is passionate about mentoring, and supports students and professionals over a range of early career stages.
Office: 158-222a
Mail Stop: 158-242
4800 Oak Grove Drive
Pasadena, CA 91109
Renewable and Clean Energy Systems (MS) – University of Dayton
Physics (BS) – University of Dayton
Imaging Spectroscopy
Machine Learning
Remote Sensing
Prior to his work at JPL, Phil worked with the Carnegie / Global Airborne Observatory on ecological and conservation projects around the globe. Phil came to remote sensing after research in multiple parts of the energy sector. Phil is passionate about mentoring, and supports students and professionals over a range of early career stages.
Find my publications on Google Scholar
Contact
Email: philip.brodrick@jpl.nasa.govOffice: 158-222a
Mail Stop: 158-242
4800 Oak Grove Drive
Pasadena, CA 91109
Education
Energy Resources Engineering (PhD) – Stanford UniversityRenewable and Clean Energy Systems (MS) – University of Dayton
Physics (BS) – University of Dayton
Research Interests
Earth ScienceImaging Spectroscopy
Machine Learning
Remote Sensing
