Nathan Ng is a quantitative researcher and postdoctoral machine learning scientist with 11 years of hands-on experience studying discrete sequence representations, robustness, and interpretability for natural language. He completed a PhD split between the University of Toronto and MIT and has held research roles at Facebook AI, Google, Meta, and Prescient Design, contributing to wins in shared tasks and practical speedups like custom CUDA kernels for sequence models. Now at Citadel, he blends academic rigor with production-facing research, focusing on fixing model pathologies and out-of-domain generalization. A multidisciplinary background in computer science, mathematics, and music and early work in bioinformatics hint at a curiosity-driven approach that pairs theoretical insight with systems engineering.
11 years of coding experience
2 years of employment as a software developer
University of California San Diego
Post Doctoral Researcher, Machine Learning, Post Doctoral Researcher, Machine Learning at New York University
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at University of Toronto
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Massachusetts Institute of Technology
High School, High School at Rancho Bernardo High School
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.