Summary
Aidan Reilly is a curious, multidisciplinary computer scientist and Physics PhD candidate at Stanford with nine years of experience applying algorithm design, machine learning, and high-performance computing to problems in high energy physics, cosmology, and computational biology. He has a strong foundation from Johns Hopkins where he earned dual top-tier degrees in Physics and Computer Science and contributed to GPU-portable scientific tools, genome-wide analysis pipelines, and parallelized bioinformatics software. At JHU APL and in academic projects he developed novel deep learning approaches for noisy, poorly labeled scientific data and implemented low-memory, one-pass algorithms for large-scale N-body simulations. Comfortable in C++, Python, CUDA, and threaded/GPU environments, he combines theoretical algorithmic analysis with pragmatic software engineering to move research into usable tools. Based in Palo Alto, he’s as likely to be optimizing radiation-transport code for supercomputers as designing approximation algorithms for network capacity, reflecting an unusual breadth across theory, experiment, and production-grade computation.
9 years of coding experience
2 years of employment as a software developer
Johns Hopkins University
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Stanford University
English, Spanish