Summary
Aaron Rumack is a data scientist with 11 years of experience applying machine learning and advanced statistics to public health problems, recently relocating to Israel after earning a PhD in Machine Learning from Carnegie Mellon. He has led the development of scalable forecasting methods for influenza, COVID-19, and RSV—implementing a generalized additive model that improved accuracy while cutting computation time by two orders of magnitude and diagnosing flaws in previous Bayesian approaches. His work spans hierarchical Bayesian spatiotemporal modeling, bias-correction of health insurance claims, and practical tooling in Python and R, with hands-on experience deploying analyses for the CDC. Known for bridging theory and practice, he combines rigorous probabilistic methods with pragmatic engineering to deliver timely, actionable forecasts to public-health stakeholders.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Cornell University
Carnegie Mellon University
English, Hebrew, Spanish