Summary
Insang Song is an Assistant Professor and spatiotemporal data scientist with nine years of experience bridging causal inference and predictive modeling for human health, focused on mental-illness-related mortality across multiple spatial scales. He develops and applies ensemble machine learning, spatial statistics, and scalable geocomputation—primarily in R—to tackle autocorrelation and missing-data problems in environmental and socioeconomic datasets. His work spans academic and government settings, including reproducible high-resolution air pollution modeling and mixture-effect analyses at NIEHS, and he has built R packages and R-Shiny tools for Bayesian spatial disease modeling. A summa cum laude graduate and near-top Ph.D. performer, he combines rigorous quantitative methods with applied public-health questions, bringing both forensic spatial thinking and hands-on software tooling to interdisciplinary teams.
9 years of coding experience
5 years of employment as a software developer
Ph.D., Geography, 4.18 / 4.30, Ph.D., Geography, 4.18 / 4.30 at University of Oregon
Master of Arts (M.A.), Geography, 4.15 / 4.30, Master of Arts (M.A.), Geography, 4.15 / 4.30 at 서울대학교
Bachelor of Arts (B.A.), Geography, 3.9 / 4.3 (summa cum laude), Bachelor of Arts (B.A.), Geography, 3.9 / 4.3 (summa cum laude) at 서울대학교 (Seoul National University)
독일어, English