University of Michigan
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December 8, 2016

Transportation, learning analytics projects get data science funding

June 22, 2016

Transportation, learning analytics projects get data science funding

Topic: Campus News

Four research projects — two each in transportation and learning analytics — have been awarded funding in the first round of the Michigan Institute for Data Science Challenge Initiatives program.

The projects will each receive $1.25 million dollars from MIDAS as part of the Data Science Initiative announced in fall 2015.

U-M Dearborn also will contribute $120,000 to each of the two transportation-related projects.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.

"These excellent research projects show the range and depth of the data-science expertise on our campuses, as well as our capacity to collaborate across disciplines," said MIDAS co-director Al Hero, professor of electrical engineering and computer science.

"They illustrate how well-positioned U-M is to advance the methodologies and applications of data science to address the grand challenges of our society," added Brian Athey, professor and chair of computational medicine and bioinformatics.

Ilir Miteza, associate provost at UM-Dearborn, said the MIDAS Challenge Initiatives program provides a valuable opportunity for collaboration.

"These projects show the combined strength of our campuses to use emerging data science techniques to address society's grand challenges," Miteza said, professor and chair of computational medicine and bioinformatics.

The projects, determined by a competitive submission process are:

Reinventing Public Urban Transportation and Mobility

Description: The project will help design and operate an on-demand, public transportation system for urban areas in which a fleet of connected and automated vehicles are synchronized with buses and light rail, using predictive models based on high volumes of diverse transportation data. The goal is to begin testing on the U-M campus within a year, and then expand the experiment to Ann Arbor and Detroit.

Lead researcher: Pascal Van Hentenryck, Industrial Operations and Engineering

Research team: Ceren Budak and Tawanna Dillahunt, School of Information; Amy Cohn, Industrial and Operations Engineering; Rebecca Cunningham, Emergency Medicine; Robert Hampshire and Jim Sayer, U-M Transportation Research Institute; Jerome Lynch, Civil and Environmental Engineering; Jonathan Levine and Louis Merlin, Taubman College of Architecture and Urban Planning;  Luis Ortiz, Computer and Information Science, UM-Dearborn; and Michael Wellman, Computer Science and Engineering.

Building a Transportation Data Ecosystem for Data Science Research and Applications

Description: The project will create a system that allows researchers to access massive, integrated datasets on transportation in a high-performance computing environment. By creating a common repository of transportation data — including data on driving, traffic, weather, accidents, vehicle messages, traffic signals and road characteristics — the project will inform the development of connected and automated vehicle systems of the future.

Lead Researcher: Carol A. Flannagan, UMTRI.

Research Team: Michael R. Elliott, School of Public Health and Institute for Social Research; Robert Hampshire and Jonathan Rupp, UMTRI; H.V. Jagadish, Jason Mars and Lingjia Tang, Computer Science and Engineering; Judy Jin, Industrial Operations and Engineering; Yi Lu Murphey, Electrical and Computer Engineering, UM-Dearborn; Kerby Shedden, Statistics; and Kristine Witkowski, ISR.

Analytics for Learners As People

Description: The project seeks to uncover connections between students' personal attributes such as values, beliefs, interests and goals, and their success in school or overall sense of well-being by expanding the traditional analysis of educational success to include other factors outside of the classroom.

Lead researcher: Rada Mihalcea, Computer Science and Engineering.

Research team:  Satinder Baveja and Emily Mower Provost, Computer Science and Engineering, Kevyn Collins-Thompson, School of Information; Daniel Eisenberg, SPH and ISR; Stuart Karabenick, School of Education; Timothy McKay, Physics, Astronomy, and Education; Perry Samson, Climate and Space Sciences and Engineering; Kerby Shedden, Statistics.

Holistic Modelling of Education

Description: Using data drawn from the university's learning technologies and related information available in the student data warehouse, the HOME project focuses on building a holistic model of student achievement, with the aim of providing instruction tailored to the specific needs of all students.

Lead researcher: Stephanie Teasley, School of Information

Research team: Christopher Brooks and Kevyn Collins-Thompson, School of Information; Gus Evrard, Physics; Anne Gere, English; Timothy McKay, Physics, Astronomy, and Education; Perry Samson, Climate and Space Sciences and Engineering.

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