Summary
Justin Stevens is a data scientist and educator with a decade of experience blending AI research, machine learning engineering, and advanced math instruction to improve student learning. He holds an M.Sc. in Computing Science from the University of Alberta where his thesis focused on explainable AI for games, and is currently advancing learning analytics at Art of Problem Solving. Justin has built ML models to assist classroom learning, developed and delivered ML curricula at Amii and universities, and authored a book on olympiad number theory, reflecting deep domain expertise in both theory and pedagogy. He has extensive experience teaching competitive math courses to diverse international cohorts and running large online classes, often designing syllabi, exams, and problem sets from scratch. Notably, his background combines published research in heuristic search and program synthesis with hands-on pedagogy, giving him a rare mix of rigorous research instincts and practical classroom impact. Based in San Diego and enrolled in a PhD program in Computational and Data Sciences, he continues to bridge cutting-edge AI methods with measurable educational outcomes.
10 years of coding experience
4 years of employment as a software developer
High School 8th-12th Grade, High School 8th-12th Grade at Davidson Academy of Nevada
University of Nevada, Reno
Doctor of Philosophy - PhD Computational and Data Sciences, Doctor of Philosophy - PhD Computational and Data Sciences at Washington University in St. Louis
Master's degree (thesis) Computing Science, Master's degree (thesis) Computing Science at University of Alberta
Harvey Mudd College