Summary
Kyle Pearson is a data scientist at JPL with 11 years of experience blending observational astronomy, machine learning, and software engineering to drive space- and Earth-science missions. He holds a PhD in planetary sciences and has built end-to-end systems—from telescope hardware and control interfaces to image-processing pipelines and Bayesian retrieval models—for exoplanet detection and atmospheric characterization. His work spans ground and space-based spectroscopy, computer vision for automated landform detection, and ML-driven remote sensing applications for climate and ecological forecasting. As a co-founder of an AR/VR startup, he has translated research-grade algorithms into real-time control and motion-capture systems, demonstrating rare full-stack instrumentation expertise. He publishes extensively (40+ works) and maintains a coding portfolio, combining rigorous academic research with production-ready cloud and pipeline implementations. Based in Altadena, CA, he brings a practical mix of hardware integration, uncertainty-aware modeling, and field-tested observational experience.
11 years of coding experience
6 years of employment as a software developer
The University of Arizona
M.S., Physics, M.S., Physics at Northern Arizona University
python, c, sql, java, html