Summary
Nicholas Carrara is a Senior Data Scientist and machine learning research scientist with a Ph.D. in Physics and eight years of experience applying CV, generative AI, reinforcement learning, and scalable ML systems to scientific problems. He combines theoretical contributions—such as first-principles mutual information derivations and a high-dimensional MI estimator using Monte Carlo Tree Search—with hands-on engineering of multi-GPU pipelines for sparse 3D reconstruction and production-ready ML code. At UC Davis and national labs he led multi-institutional experiments (DUNE, ARTIE, MArEX) and built ML-driven signal detection and reconstruction tools for particle and neutron physics. Equally comfortable with contrastive learning, GNNs, transformers, and diffusion models, he bridges research and product by optimizing large-scale simulation stacks (LArSoft, PyTorch) for real experimental throughput. Based in Sacramento, he’s now applying this blend of theoretical depth and systems engineering at Deloitte to build intelligent systems with measurable scientific and operational impact.
8 years of coding experience
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at University at Albany
Associate of Science - AS, Biology, General, Magna Cum Laude, Associate of Science - AS, Biology, General, Magna Cum Laude at Hudson Valley Community College