Amanda Baxter is a PhD physicist finishing at Purdue University with a decade of experience applying Python-driven data analysis and software development to particle and astroparticle experiments. Her research characterizes single- and few-electron backgrounds in liquid xenon detectors to push sensitivity to light dark matter and has produced a real-time supernova trigger prototype that integrates XENONnT into SNEWS using modern publish-subscribe tooling. Comfortable in Agile teams, she combines large-data analysis, experiment-facing software, and GUI/automation experience from national lab projects to classroom mentorship. She is particularly adept at translating low-level detector behavior into robust analysis selections and operational alert systems, making her equally valuable for scientific software, observatory infrastructure, or aerospace data challenges.
9 years of coding experience
Master of Science - MS, Physics, Master of Science - MS, Physics at Purdue University
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Siena College
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