Machine learning and artificial intelligence wouldn’t be possible without the statistical models that underpin their analytic capabilities. A Cornell statistician and his colleague have developed a revolutionary new method to analyze complex datasets that’s more flexible, accurate and easy to use. 

Dan Kowal, associate professor of statistics and data science, a shared department in the College of Agriculture and Life Sciences and the Cornell Ann S. Bowers College of Computing and Information Science, is lead author of “Monte Carlo Inference for Semiparametric Bayesian Regression,” which published Oct. 1 in the Journal of the American Statistical Association. Co-author is Bohan Wu, now a Ph.D. student at Columbia University. 

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