Statistical and Algorithmic Tools to Aid Recovery in Flint

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.
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.

Eric Schwartz is an Assistant Professor in the Marketing area at the Ross School of Business of the University of Michigan, Ann Arbor. His primary interests are in customer-base analysis and online marketing experiments. He enjoys finding applications of statistical machine learning and adaptive sampling problems. He earned his PhD in Marketing from Wharton School of Business of the University of Pennsylvania.