Summary
Daniel Hathcock is a PhD-trained algorithms and optimization researcher with 11 years of hands-on experience applying theory to practical systems, currently based in the Albany, NY area. His work spans approximation algorithms, combinatorics, and machine learning—ranging from provable results about acyclic orientations to neural time-to-event models built with JAX and TensorFlow. He has taught calculus, discrete math, algorithms, and linear algebra at the university level and mentored students through office hours and recitations. In industry roles he sped up distributed model training by over 10x using Dask and prototyped navigation and perception software for autonomous robots on Raspberry Pi. Comfortable moving between rigorous proofs and production code, he blends deep theoretical intuition with pragmatic engineering. An uncommon thread through his career is leveraging randomized heuristics and probabilistic methods to bridge pure math insights with scalable implementations.
11 years of coding experience
2 years of employment as a software developer
Bachelors of Science Computer Science, Bachelors of Science Computer Science at Georgia Institute of Technology
Doctor of Philosophy - PhD Algorithms Combinatorics and Optimization (ACO), Doctor of Philosophy - PhD Algorithms Combinatorics and Optimization (ACO) at Carnegie Mellon University