Alex Diaz-papkovich is a statistician and data scientist with 12 years of experience applying mathematical and computational methods to large-scale human genetics and single-cell sequencing data. Currently a postdoctoral research associate at Brown University's Data Science Institute, he blends rigorous theoretical training (PhD, McGill; MSc, Carleton) with practical survey-methods experience from Statistics Canada to develop and implement estimation, imputation, and sampling strategies. His work spans statistical methods development and computational biology, with fluency in R, SAS, and scripting tools and a knack for translating abstract probabilistic ideas into reproducible workflows. Based in Providence, he pairs deep quantitative expertise with hands-on data engineering from earlier roles in industry, making him effective at both theory-driven research and production-ready analysis. An understated strength is his longevity across academic and government settings, which gives him perspective on both methodological rigor and operational constraints.
12 years of coding experience
7 years of employment as a software developer
Master of Science (M.Sc.), Probability and Statistics, Master of Science (M.Sc.), Probability and Statistics at Carleton University
Bachelor of Mathematics, Statistics, Bachelor of Mathematics, Statistics at University of Waterloo - St. Jerome's University
Doctor of Philosophy - PhD, Quantitative Life Sciences, Doctor of Philosophy - PhD, Quantitative Life Sciences at McGill University
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Alex Diaz-papkovich - Postdoctoral Research Associate