Ryan Friedman is a computational biologist and postdoctoral scholar specializing in gene regulation, combining expertise in supervised and active learning, model interpretation, and sequencing-based assays such as Massively Parallel Reporter Assays. With a PhD in Computational and Systems Biology and a decade of experience spanning Washington University and the University of Washington, he builds reproducible analysis pipelines that bridge machine learning and experimental genomics. He brings domain knowledge in transcription factor biology and a practical focus on accessible, colorblind-friendly data visualization. Unusually for an academic computationalist, he pairs hands-on high-throughput sequencing pipeline development with active-learning approaches to prioritize experiments, accelerating discovery at the interface of data and wet-lab biology.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Arts - BA, Genomics & Computational Biology, Computer Science, 3.88, Bachelor of Arts - BA, Genomics & Computational Biology, Computer Science, 3.88 at Washington University in St. Louis
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Ryan Friedman - Postdoctoral Scholar at University of Washington