Camille Parisel is a seasoned research engineer with 12+ years applying AI and scientific computing to astrophysics, remote sensing, and bioinformatics, currently operating with the IceCube collaboration at the University of Wisconsin–Madison. She has led multisite teams to put deep learning into production for national mapping and remote sensing programs, and built shared AI infrastructure and CI/CD workflows for research labs and observatory instruments. Her background blends hands-on embedded and data-acquisition systems (from Antarctic fieldwork to large detector arrays) with advanced deployments on high-performance computing centers like Jean Zay. Camille pairs developer-first pragmatism with academic rigor—teaching, mentoring at hackathons, and supporting European research projects—so she moves prototypes to reliable, auditable pipelines. Fluent in Python and DevOps practices, she’s as comfortable tuning a 3.2 Gpx camera filter changer as she is containerizing ML workflows for production. An early IA enthusiast since Karl Sims’ Virtual Creatures, she uniquely bridges simulation, instrumentation and operational AI at scale.
12 years of coding experience
4 years of employment as a software developer
DEA Intelligence artificielle, DEA Intelligence artificielle at Université Henri Poincaré, Nancy 1
Diplôme d'ingénieur Spécialisation en géomatique/devops ouverture sur les enjeux du développement durable, Diplôme d'ingénieur Spécialisation en géomatique/devops ouverture sur les enjeux du développement durable at Ecole nationale des Travaux publics de l'Etat
Master en Géomatique Spécialisation Systèmes d'Information, Master en Géomatique Spécialisation Systèmes d'Information at Ecole nationale des Sciences géographiques
Contributions:5 releases, 198 commits, 171 pushes in 2 years 5 months
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