MIDAS Research Hubs

Building on the expertise of U-M faculty,
focusing on innovative data science tools and methodologies.

MIDAS Research Hub: Transportation

The Next Generation of Mobility

The mission of the MIDAS Transportation Research Hub is to harness the power of big data to help develop the next generation of mobility, from driverless and autonomous vehicles to integrated multi-modal transportation systems. The Hub is partially funded by the U-M Data Science Initiative, which, among other efforts, provides faculty research grants for projects likely to garner significant investment from industry or government.

MIDAS Research Hub: Health Science

Advancing the spectrum of biomedical research

The MIDAS Health Science Research Hub aims to enhance U-M biomedical researchers’ ability embrace the opportunities and challenges brought about by advances in data science. The Hub will catalyze the development of theories and innovative methodologies in data science, and their application to the entire spectrum of biomedical science, from basic research to translation research, to clinical applications, with the ultimate goal of using data science to improve the health of our society.

MIDAS Research Hub: Learning Analytics

Improving education through data

The MIDAS Learning Analytics Research Hub helps U-M faculty take advantage of the trove of student data collected by the University over the years to improve educational practices and student outcomes, and is partially funded by the U-M Data Science Initiative.

MIDAS Research Hub: Social Science

Leading the evolution of social research

The data science revolution is bringing unprecedented opportunities to social science research.  Never before have we had such enormity and variety of data, from endless social media streams to every survey imaginable, from outputs of every conceivable sensor and wireless device to massive consumer databases. We are seeing data in every form, every level of granularity and quality.  Social scientists’ newest challenge is to generate insight from the data for the political, economic and social wellbeing of individuals and our society.

MIDAS Research Hub: Music

Connecting data science and music

U-M has incredible depth in data science expertise and a world-class School of Music, Theatre and Dance.  With this initiative, MIDAS helps U-M scientists lead the nation the research at the intersection of data science and music.

Research News

See a complete listing of publications from MIDAS-affiliated faculty via Google Scholar.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

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Title 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification Published in Scientific Reports 8, October 2018 DOI 10.1038/s41598-018-33574-w Authors Alexandr A. Kalinin, Ari Allyn-Feuer, Alex Ade, Gordon-Victor Fon,…

The effectiveness of parking policies to reduce parking demand pressure and car use

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This study is a part of the "Reinventing Transportation and Urban Mobility" project, funded by the Michigan Institute for Data Science. Title The effectiveness of parking policies to reduce parking demand…

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

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This research was supported by funding from the Michigan Center for Single-Cell Genomic Data Analytics—a part of the Michigan Institute for Data Science. Title VIPER: variability-preserving imputation for accurate gene…

TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction

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MIDAS-affiliated researchers recently published a paper on accurate and fast computational tools for predicting drug synergistic effects. Title TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction Published in Bioinformatics, November…

MIDAS announces winners of 2018 poster competition

| Educational, General Interest, Happenings, Research | No Comments
The Michigan Institute for Data Science (MIDAS) is pleased to announce the winners of its 2018 poster competition, which is held in conjunction with the MIDAS annual symposium. The symposium…

MDST group wins KDD best paper award

| General Interest, Happenings, MDSTPosts, Research | No Comments
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…