Nima Hejazi is an assistant professor of biostatistics at Harvard Chan with 11 years of applied and methodological experience translating causal inference and statistical machine learning into reproducible tools for biomedical and public health discovery. His work emphasizes assumption-lean, model-agnostic inference—combining semi-parametric efficiency theory with flexible machine learning—to answer interpretable scientific estimands in trials and observational studies. He has translated these interests into open-source software and high-performance computing efforts that promote transparency across diverse collaborations from vaccine efficacy and infectious disease to environmental and nutritional epidemiology. A seasoned educator and communicator, he blends academic rigor from a PhD at UC Berkeley with industry experience including causal ML internships at Netflix and experimentation work at Pandora. Less obvious: he routinely bridges theory and practice by designing estimators robust to biased or outcome-dependent sampling (e.g., case-cohort) while maintaining practical implementability for real-world studies.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at University of California, Berkeley
:wrench: :computer: personalized dumpster fire of config files for Linux and macOS
Contributions:2 PRs, 451 pushes, 3 branches in 9 years 4 months
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Nima Hejazi - Assistant Professor Of Biostatistics