ABSTRACT: Recovery from the Flint Water Crisis has been hindered by uncertainty in both the water testing process and the causes of contamination. On the other hand, city, state, and federal officials have been collecting and organizing a significant amount of data, including many thousands of water samples, information on pipe materials, and city records. Combining all of this information, and utilizing state-of-the-art algorithmic and statistical tools, we have be able to develop a clearer picture as to the source of the problems, to accurately estimate the greatest risks, and to more efficiently direct resources towards recovery.
Bio: Jacob Abernethy is an Assistant Professor in the EECS Department at the University of Michigan, Ann Arbor. He finished his PhD in Computer Science at the UC Berkeley, and was a Simons postdoctoral fellow at the University of Pennsylvania. Jake’s primary interest is in Machine Learning, and he likes discovering connections between Optimization, Statistics, and Economics.
Bio: Eric Schwartz is an Assistant Professor of Marketing at the University of Michigan’s Ross School of Business in Ann Arbor. He received his PhD in Marketing from the Wharton School at the University of Pennsylvania in 2013. His research focuses on predicting customer behavior, understanding its drivers, and examining how firms actively manage their customer relationships through interactive marketing. The quantitative methods he uses are primarily Bayesian statistics, machine learning, dynamic programming, and field experiments.