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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)

Schedule:

9 am: Introduction

9:05 am to 11:30 am: Challenge Award presentations

11:30 am to 11:45 am: Break

11:45 am to 12:45 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
  • Matthew Shapiro, Professor, Economics, University of Michigan
  • Lisa Singh, Professor, Computer Science, Georgetown University
  • Mike Traugott, Professor Emeritus, Communication Studies, Political Science, University of Michigan

12:45 pm to 2 pm: lunch, poster session and networking (Please fill out this form to submit a poster; deadline is Monday, September 10; poster size can be up to 4 ft high, 6 ft wide)

MIDAS Health Sciences Challenge Symposium

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Data-intensive health 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.  It will feature in-depth presentations from three Challenge Award teams, and all participants are encouraged to submit posters on data-intensive health science research.

Agenda

9 am to 12:50 pm: Welcome and Challenge Award Presentations

12:50 pm to 2:15 pm: Lunch, Poster Session and Networking [poster dimensions: up to 6ft wide X 4ft height]

2:15 pm: Panel Discussion: The Future of Data-intensive Health Sciences at U-M.

  • Panelists: Brian Athey (Moderator), Marisa Eisenberg, Jun Li, Brahmajee Nallamothu, Srijan Sen, Kevin Ward

Please register online.  Please submit poster abstracts (< 300 words).  Submission Deadline: April 28.

For questions: midas-research@umich.edu.

MIDAS announces second round of Data Science Challenge Initiative awards, in health and social science

By | General Interest, News, Research

Five research projects — three in health and two in social science — have been awarded funding in the second round of the Michigan Institute for Data Science Challenge Initiative program.

The projects will receive funding from MIDAS as part of the Data Science Initiative announced in fall 2015.

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. For more information, visit midas.umich.edu/challenges.

The projects, determined by a competitive submission process, are:

  • Title: Michigan Center for Single-Cell Genomic Data Analysis
    Description: The center will establish methodologies to analyze sparse data collected from single-cell genome sequencing technologies. The center will bring together experts in mathematics, statistics and computer science with biomedical researchers.
    Lead researchers: Jun Li, Department of Human Genetics; Anna Gilbert, Mathematics
    Research team: Laura Balzano, Electrical Engineering and Computer Science; Justin Colacino, Environmental Health Sciences; Johann Gagnon-Bartsch, Statistics; Yuanfang Guan, Computational Medicine and Bioinformatics; Sue Hammoud, Human Genetics; Gil Omenn, Computational Medicine and Bioinformatics; Clay Scott, Electrical Engineering and Computer Science; Roman Vershynin, Mathematics; Max Wicha, Oncology.
  • Title: From Big Data to Vital Insights: Michigan Center for Health Analytics and Medical Prediction (M-CHAMP)
    Description: The center will house a multidisciplinary team that will confront a core methodological problem that currently limits health research — exploiting temporal patterns in longitudinal data for novel discovery and prediction.
    Lead researchers: Brahmajee Nallamothu, Internal Medicine; Ji Zhu, Statistics; Jenna Wiens, Electrical Engineering and Computer Science; Marcelline Harris, Nursing.
    Research team: T. Jack Iwashyna, Internal Medicine; Jeffrey McCullough, Health Management and Policy (SPH); Kayvan Najarian, Computational Medicine and Bioinformatics; Hallie Prescott, Internal Medicine; Andrew Ryan, Health Management and Policy (SPH); Michael Sjoding, Internal Medicine; Karandeep Singh, Learning Health Sciences (Medical School); Kerby Shedden, Statistics; Jeremy Sussman, Internal Medicine; Vinod Vydiswaran, Learning Health Sciences (Medical School); Akbar Waljee, Internal Medicine.
  • Title: Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology
    Description: Using an app platform that integrates signals from both mobile phones and wearable sensors, the project will collect data from over 1,000 medical interns to identify the dynamic relationships between mood, sleep and circadian rhythms. These relationships will be utilized to inform the type and timing of personalized data feedback for a mobile micro-randomized intervention trial for depression under stress.
  • Lead researchers: Srijan Sen, Psychiatry; Margit Burmeister, Molecular and Behavioral Neuroscience.
    Research team:  Lawrence An, Internal Medicine; Amy Cochran, Mathematics; Elena Frank, Molecular and Behavioral Neuroscience; Daniel Forger, Mathematics; Thomas Insel (Verily Life Sciences); Susan Murphy, Statistics; Maureen Walton, Psychiatry; Zhou Zhao, Molecular and Behavioral Neuroscience.
  • Title: Computational Approaches for the Construction of Novel Macroeconomic Data
    Description: This project will develop an economic dataset construction system that takes as input economic expertise as well as social media data; will deploy a data construction service that hosts this construction tool; and will use this tool and service to build an “economic datapedia,” a compendium of user-curated economic datasets that are collectively published online.
    Lead researcher: Matthew Shapiro, Department of Economics
    Research team: Michael Cafarella, Computer Science and Engineering; Jia Deng, Electrical Engineering and Computer Science; Margaret Levenstein, Inter-university Consortium for Political and Social Research.
  • Title: A Social Science Collaboration for Research on Communication and Learning based upon Big Data
    Description: This project is a multidisciplinary collaboration meant to introduce social scientists, computer scientists and statisticians to the methods and theories of engaging observational data and the results of structured data collections in two pilot projects in the area of political communication and one investigating parenting issues. The projects involve the integration of geospatial, social media and longitudinal data.
    Lead researchers: Michael Traugott, Center for Political Studies, ISR; Trivellore Raghunathan, Biostatistics
    Research team: Leticia Bode, Communications, Georgetown University; Ceren Budak, U-M School of Information; Pamela Davis-Keane, U-M Psychology, ISR; Jonathan Ladd, Public Policy, Georgetown; Zeina Mneimneh, U-M Survey Research Center; Josh Pasek, U-M Communications; Rebecca Ryan, Public Policy, Georgetown; Lisa Singh, Public Policy, Georgetown; Stuart Soroka, U-M Communications.

For more details, see the press releases on the social science and health science projects.

Transportation projects with MIDAS funding featured in “Urban Transportation Monitor”

By | General Interest, News

Research on connected and automated vehicles which recently received funding under the Michigan Institute for Data Science (MIDAS) Challenge Initiative was featured in “The Urban Transportation Monitor.” The projects featured are led by Pascal Van Hentenryck (Industrial and Operations Engineering) and Carol Flannagan (U-M Transportation Research Institute).

The article, available for download, begins on page 6.

The Urban Transportation Monitor (now in its 30th year) reports on the latest developments in urban transportation. Its circulation reaches thousands of readers across the U.S. as well as 30 countries worldwide. Subscribers include the most prominent organizations active in urban transportation. For more information, see www.urban-transportation-monitor.com/.

MIDAS Challenge Initiative Awards featured on Xconomy news website

By | General Interest, News

The Xconomy.com website has featured transportation data science projects awarded funding under the first round of the MIDAS Challenge Initiatives.

College of Engineering Prof. Pascal Van Hentenryck and Carol Flannagan of the U-M Transportation Research Institute were awarded $1.25 million each for projects that will apply data science methodologies to transportation. U-M Dearborn is contributing another $120,000 to each project, and co-PIs are involved from the School of Information, Medical School, Architecture and Urban Planning, Computer Science, School of Public Health, and the Institute for Social Research, among others.

Please see the Xconomy article for more.

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.