My journey started by analyzing data from the depths of Antarctica, where I built systems to hunt for cosmic neutrinos - nature's most elusive particles - using detectors buried 669 feet deep in polar ice. As an undergraduate, I developed the processing framework for the Askaryan Radio Array experiment at the South Pole – managing petabytes of data in search of these ghost particles. I leveraged supercomputers and developed reconstruction algorithms to find neutrino positions based on their interactions in deep ice. Recognized with the Outstanding Achievement Award in Cornell's Computer Science Master's program, I learned to blend theoretical excellence with practical innovation. This pursuit of excellence led me to Meta, working with the same infrastructure behind Instagram and WhatsApp's search capabilities, I architected knowledge recommendation systems at scale and drove key initiatives in a landmark comparison between Meta's search infrastructure and Google's Google Cloud Search, gaining unique rare insights into how the two world's tech giants handle search at scale. Today, I combine the precision of particle physics, the operational scale of Meta's systems, and the innovation of modern AI to tackle two of computing's greatest challenges: systematically constraining AI hallucinations and systematically scaling AI with zero costs. Drawing from years of experience hunting cosmic neutrinos, I develop frameworks that will make AI systems reliable and transformative, leading the way forward in automating and enhancing knowledge work.