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Data-Intensive Social Science Challenge Symposium

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Data-intensive social science is one of the research focus areas that MIDAS supports with its Challenge Awards. Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass. This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub. The two Challenge Award teams will give in-depth presentations, and all participants are encouraged to submit posters on research related to data-intensive social science.

Registration | Poster submission form (Due Monday, Sept. 10)

Preliminary Schedule:

9 am: Introduction

9:05 am to 11:35 pm: Challenge Award presentations

11:35 am to 1 pm: lunch, poster session and networking (Please fill out this form to submit a poster; deadline is Monday, September 10)

1 to 2 pm: Panel discussion: the future of data-intensive social science research at U-M

  • Martha Bailey, Professor, Economics, University of Michigan
  • Sara Heller, Assistant Professor, Economics, University of Michigan
  • Matt Shapiro, Professor, Economics, University of Michigan
  • Lisa Singh, Professor, Computer Science, Georgetown University
  • Mike Traugott, Professor Emeritus, Communication Studies, Political Science, University of Michigan

MIDAS awards first round of challenge funding in transportation and learning analytics

By | General Interest, Happenings, 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.