Dr. LaHaye is a data scientist at NASA’s Jet Propulsion Laboratory in Pasadena, California. As a member of the Machine Learning and Instrument Autonomy Group, Dr. LaHaye supports active and future missions, as well as application-driven research that aims to aid researchers and decision-makers. Currently, Dr. LaHaye leads an effort to leverage self-supervised models and multi-sensor datasets to segment and track object instances (wildfire fronts, plumes, harmful algal blooms, palm oil farms) in low and no-label environments. This effort strives to integrate both on-the-ground research and decision-making applications, as well as next-generation onboard retrieval capabilities. He also currently works on data-driven retrieval techniques for 3D cloud tomography and data-driven hydrological retrieval and validation techniques for Sentinel-6 and SWOT.

Previously, Dr. LaHaye split his time between research and the development, generalization, and maintenance of pieces of the operational science data systems for missions like MISR, Jason-3, and SWOT, as well as airborne programs like AirMSPI and HyTES.

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Education

• Computational and Data Sciences (PhD) - Chapman University
• Computer Science (MS) - University of Souther California
• Mathematics & Computer Science (BS) - Chapman University

Research Interests

• Machine Learning / Deep Learning
• Remote Sensing
• Explainability
• Self-Supervision
• Data-Driven Science