Vincent Dorie
About Vincent Dorie
Vincent Dorie is a Principal Data Scientist at Code for America, specializing in Bayesian nonparametrics, causal inference, and mixed models. He has a strong background in statistics, having earned a PhD from Columbia University and worked in various academic and consulting roles.
Work at Code for America
Vincent Dorie has been serving as the Principal Data Scientist at Code for America since 2020. In this role, he focuses on leveraging data to enhance government human service delivery within the not-for-profit sector. His work involves developing statistical software that emphasizes Bayesian nonparametrics, causal inference, and mixed models, contributing to the organization's mission of improving public services through data-driven approaches.
Education and Expertise
Vincent Dorie holds multiple degrees in the field of statistics and computer science. He earned a PhD in Statistics from Columbia University, where he studied from 2008 to 2014. He also obtained an M.A. in Statistics from Columbia University in 2009. Prior to that, he completed a B.S. in Computer Science at Stanford University from 2000 to 2004 and an M.S. in Biomedical Informatics from Stanford University School of Medicine in 2005.
Background
Vincent Dorie has a diverse professional background that includes various academic and research positions. He worked as an Adjunct Instructor at New York University for three months in 2020 and served as a Software Consultant at the same institution from 2017 to 2018. His academic career at Columbia University included roles as an Adjunct Research Scientist, Associate Research Scientist, and Adjunct Professor of Statistics, spanning from 2018 to 2021.
Achievements
Throughout his career, Vincent Dorie has developed expertise in statistical methodologies, particularly in Bayesian nonparametrics and causal inference. His contributions to the field include the development of statistics software aimed at improving data analysis and interpretation. His focus on enhancing government services through data applications reflects his commitment to using statistical knowledge for societal benefit.