Adjunct Lecturer in International and Public Affairs
Director of Data Science, Lead Data Scientist, Brown University
Areas of Interest: Statistical inference, ensemble methods for machine learning, high-performance computing, parallel algorithm design, GPU programming, environmental policy
Paul Stey is a data scientist currently serving as the director of scientific computing and data science at Brown University's Center for Computation and Visualization. He is broadly interested in high-performance computing and parallel algorithm design, as well as methods for handling common challenges in statistical inference (e.g., missingness, class imbalance, etc.). Prior to coming to Brown, he worked as a statistician at the U.S. Environmental Protection Agency, and he continues to work on projects related to environmental policy. Stey did his undergraduate work at The Ohio State University, and completed a PhD in developmental psychology at the University of Notre Dame.
MPA 2065 Introduction to Data Science and Programming