Elaad Applebaum is an astrophysicist-turned-data scientist with 8 years of experience applying machine learning to industrial IoT problems at Very, where he now serves as Lead Data Scientist II. He combines rigorous scientific modeling from a PhD in physics with hands-on production engineering to deliver creative, interpretable ML solutions that run reliably end-to-end for smart manufacturing, energy, and consumer devices. His research background includes large-scale numerical simulations, bias-corrected inference methods, and contributions to open-source astrophysics tools, which inform his disciplined approach to feature engineering and uncertainty quantification. Known for lifting model accuracy while keeping computational overhead low, he also mentors teams and has a track record of turning complex, research-grade techniques into practical products.
8 years of coding experience
11 years of employment as a software developer
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at University of Illinois Urbana-Champaign
Doctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at Rutgers University
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