Generable
Eric is an applied statistician and entrepreneur with many years of experience building and explaining statistical models in healthcare, financial services, and retail verticals. He is passionate about Bayesian inference, decision theory, and making complex models useful to decision makers. Eric is a mentor in the Columbia University’s Statistics Department and taught seminars at the Quantitative Methods in the Social Sciences (QMSS) program at Columbia. As a teenager, Eric was on the leading junior cycling team in Latvia.
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Generable
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The Generable platform is utilizing the latest advances in probabilistic programming, Bayesian inference, and multi-level regression and poststratification (MRP) for dose optimization, making go/no-go decisions from early clinical data, assessing the predictive power of biomarkers, and performing principled subgroup analysis.