MDST group wins KDD best paper award

By | General Interest, Happenings, MDSTPosts, Research

A paper by members and faculty leaders of the Michigan Data Science Team (co-authors: Jacob Abernethy, Alex Chojnacki, Arya Farahi, Eric Schwartz, and Jared Webb) won the Best Student Paper award in the Applied Data Science track at the KDD 2018 conference in August in London.

The paper, ActiveRemediation: The Search for Lead Pipes in Flint, Michigan, details the group’s ongoing work in Flint to detect pipes made of lead and other hazardous material.

For more on the team’s work, see this recent U-M press release.

U-M professors and students develop app to help Flint residents identify lead risks

By | General Interest, News

A mobile app and website built for the city of Flint is available now to help the community and government agencies manage the ongoing water crisis.

Mywater-Flint, for Android and online at Mywater-flint.com, was developed by computer science researchers at the University of Michigan’s Flint and Ann Arbor campuses and funded by Google.org. Through it, residents and city employees can:

  • Access a citywide map of where lead has been found in drinking water.
  • Discover where service line workers have replaced infrastructure that connects. homes to the water main, and where they’re currently working.
  • Locate the nearest distribution centers for water and water filters.
  • Find step-by-step instructions for water testing.
  • Determine the likelihood that the water in a home or another location is contaminated, among other features.

Jacob Abernethy and Eric Schwartz: Statistical and Algorithmic Tools to Aid Recovery in Flint

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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.

U-M Professors Jacob Abernethy and Eric Schwartz to speak on “Statistical and Algorithmic Tools to Aid Recovery in Flint” — Sept. 12

By | Educational, Events, General Interest, News

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.