Speaker Profile

Daniel Lee spends his days building statistical models at Zelus Analytics. He is a computational Bayesian statistician who helped create and develop Stan. He has 20 years of experience in numeric computation and software and over 10 years of experience creating and working with Stan. Past projects include sabermetrics for an MLB team; assessing “clutch” moments in NFL footage; estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; and data fusion for U.S. Navy applications. He’s won the 2016 Sports Illustrated Hackathon and MIT SSAC 2022 Hackathon. Daniel has led workshops and given talks in applied statistics and Stan at Columbia University, MIT, Penn State, UC Irvine, UCLA, University of Washington, Vanderbilt University, Amazon, Climate Corp, Swiss Statistical Society, IBM AI Systems Day, R/Pharma, StanCon, PAGANZ, ISBA, PROBPROG, and NeurIPS. He holds a B.S. in Mathematics with Computer Science from MIT, and a Master of Advanced Studies in Statistics from Cambridge University.