Summary
Tony Wong is a computational scientist and educator with 9+ years applying machine learning, Bayesian methods, and reproducible data pipelines to high-impact problems in climate risk, ecosystem hydrology, and educational equity. As an Assistant Professor and Learning Assistant Program Director at RIT, he builds open-source modeling tools and scalable calibration/validation workflows that integrate messy observational, satellite, and climate model data. His research blends ensemble and uncertainty quantification techniques to inform policy-relevant decisions—such as sea-level rise risk—while translating results for diverse stakeholders from peer-reviewed publications to institutional retention analyses. Tony’s background in applied mathematics and hands-on teaching across multiple universities gives him a rare combination of statistical rigor, pedagogy, and software engineering. He is motivated by mission-driven applications of data science where computation directly improves resilience and access, and he often draws on tracer-based process understanding in model development—an approach born from his PhD work on stable water isotopes.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Applied Mathematics, Doctor of Philosophy (Ph.D.), Applied Mathematics at University of Colorado at Boulder
Bachelor's Degree, Astrophysics, Mathematics (Hons.), Bachelor's Degree, Astrophysics, Mathematics (Hons.) at Ohio Wesleyan University
English