Summary
Alan Pearl is a Principal Data Scientist and former astrophysics postdoc who applies nine years of experience in scientific software, parallel ML, and statistical inference to production planning and large-scale data problems. After earning a PhD from the University of Pittsburgh and building generative, differentiable deep-learning pipelines at Argonne to model galaxy formation, he transitioned to industry work at Alaska Airlines and AI training for physics prompts. He leads several open-source Python projects and has a track record of turning complex cosmological simulations and mock catalogs into efficient, gradient-optimized workflows. Comfortable at the intersection of research and production, he brings deep domain expertise in generative modeling, normalizing flows, and computational scaling to solve practical business problems. An underappreciated strength is his ability to compress high-dimensional scientific insight into reproducible software that teams can deploy and iterate on.
9 years of coding experience
2 years of employment as a software developer
Manchester High School
B.S., Physics, B.S., Physics at Rensselaer Polytechnic Institute
M.S., Ph.D., Physics, M.S., Ph.D., Physics at University of Pittsburgh
English, French, Spanish