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About

Jay Gao is a researcher whose work spans two distinct and impactful domains: autonomous robotics and atmospheric aerosol science. In robotics, Gao was a key contributor to TEAM CoSTAR’s success in the DARPA Subterranean Challenge, as detailed in the highly cited 2021 paper “NeBula: Quest for Robotic Autonomy in Challenging Environments” (105 citations). This work advanced the algorithms, hardware, and software architectures enabling robots to navigate and operate autonomously in extreme, GPS-denied underground environments. In parallel, Gao has made significant contributions to environmental remote sensing, focusing on the retrieval and analysis of aerosol optical depth (AOD) from satellite data. His 2014 paper on “Aerosol Indices Derived from MODIS Data” (21 citations) provided a method for using MODIS imagery to indicate aerosol-induced air pollution, while his subsequent work on synergetic retrieval from Terra and Aqua satellites (11 citations) and sensitivity studies of PARASOL data (5 citations) further refined techniques for monitoring atmospheric particulates. This dual expertise—pushing the boundaries of robotic autonomy in the most challenging terrains while developing tools to understand and mitigate air pollution—demonstrates a rare and valuable breadth, with his robotics work in particular having a strong impact on the field of field robotics.