Ali Turfah is a Biostatistics PhD student and Graduate Student Research Assistant at the University of Michigan with a decade of experience applying statistical methods and machine learning to medical data. His work spans clinical and biomedical informatics—from mining and automated annotation of medical literature at Columbia to COVID-19 vaccine adverse event research and imaging analysis at Michigan and Mount Sinai. He combines rigorous statistical theory (multiclass Bayesian classifiers for high-dimensional, low-sample problems) with practical data science roles in industry and academia, bridging methodology and translational impact. Known for tackling noisy, small-sample biomedical datasets, he emphasizes interpretable models that inform clinical questions. Based in Ann Arbor, he leverages a strong academic foundation (MA Statistics, Columbia; BE Data Science) to move research toward real-world medical applications.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Biostatistics, Doctor of Philosophy - PhD, Biostatistics at University of Michigan
Master of Arts - MA, Statistics, 3.93, Master of Arts - MA, Statistics, 3.93 at Columbia University in the City of New York
Bachelor of Engineering (B.E.), Data Science, 3.31, Bachelor of Engineering (B.E.), Data Science, 3.31 at University of Michigan College of Engineering
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