Data science and AI methodologies are transforming many research fields, blurring disciplinary boundaries and driving scientific discoveries through new perspectives. MIDAS affiliate faculty researchers are using cutting-edge methodologies for a variety of “endpoints” – significant scientific and societal challenges. MIDAS helps them develop new ideas and interdisciplinary teams through research incubation activities, pilot funding and dataset access. The examples below exhibit our faculty members’ creative and impactful research toward three “endpoints”.

Health Equity

  • Dr. Nancy Fleischer (Epidemiology), Dr. Karandeep Singh (Learning Health Sciences) and other MIDAS faculty members use public health survey data and medical record data to uncover inequity in COVID patients’ experiences, the care they receive, and long-term impacts.
  • Dr. Rahul Ladhania (Health Management and Policy) and colleagues are improving Machine Learning methods to better predict persistent opioid use for all race and gender groups
  • Dr. David Jurgens (School of Information) and colleagues use advanced Natural Language Processing methods to improve racial equity in doctor-patient interactions.
  • Dr. Jon Zelner (Epidemiology) uses geospatial data to provide guidance to the State of Michigan on the unequal infection risks of different racial groups.

The Environment and Sustainable Development

  • Dr. Neil Carter (Environment and Sustainability) and colleagues combine many types of data and use probabilistic modeling to figure out how illicit wildlife trade networks operate.
  • Dr. Paramveer Dhillon (School of Information) and colleagues use Graph Neural Networks to analyze environmental sensor data to predict air quality during natural disasters.
  • Dr. Ayumi Fujisaki-Manome (Climate and Space Sciences and Engineering) and colleagues develop machine learning models for ice forecasting to support the Great Lakes shipping community.
  • Dr. Joshua Newell (Environment and Sustainability) and colleagues use geospatial data and social media data and Deep Learning to identify communities that are vulnerable to flooding and heat, and plot climate change vulnerability maps for the State of Michigan.

Protecting Democracy

  • Dr. Libby Hemphill (School of Information) and colleagues analyze social media data to identify extremist groups.
  • Dr. Rada Mihalcea (Computer Science and Engineering) and colleagues develop AI algorithms based on linguistic analysis of media and online data to automatically detect fake news.
  • Dr. Sarita Schoenebeck (School of Information) and colleagues analyze large volumes of social media data and design large-scale public experiments to reduce harassment and the spread of misinformation online.
  • Dr. Michael Traugott (Center for Political Studies) and colleagues analyze media reports and social media data to determine the political sentiments of the public during major political campaigns.

The 2021 round of Propelling Original Data Science pilot grant program supports 17 research projects, involving 36 (co-) Principal Investigators from 7 schools / colleges at Ann Arbor and Flint. Since 2015, MIDAS research funding has enabled a total of 52 research projects, and the project teams have gone on to secure more than $100,000,000 in funding from external grants.

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Machine Learning in Drug Discovery

Through the NSF’s Industry–University Cooperative Research Centers (IUCRC) program, MIDAS Associate Director Dr. Kayvan Najarian is leading the effort to develop the Center for Data-Driven Drug Development and Treatment Assessment (DATA). It brings together data scientists, mathematicians, biomedical researchers, and healthcare providers to produce reproducible methodologies that will make a broad impact on drug discovery and biomedical applications of data science. By forming collaborations with industry, government, and community partners, the project will enable the dissemination and translation of research into impactful products and services for the betterment of society. DATA will also build a world-class data science community that is inclusive and promotes diversity at all stages of the academic pipeline.

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Research with Social Media Data

Through an arrangement with Twitter, MIDAS enables U-M researchers to access a compilation of tweets known as the “Decahose” (a 10% sample of all tweets) for research on information diffusion, human behavior and Sociolinguistics, as well as research with Natural Language Processing and network analysis. This dataset has enabled >40 research projects on the freedom to protest, the impact of educational programs on child sexual abuse, cultural appropriation, the role major sports events play on sex trafficking, the interplay between interpersonal relationships and unexpected life events, as well as many other research topics.

See how to access the Decahose

Michigan Data Science Fellows

This postdoc program at MIDAS provides outstanding young researchers with intensive data science and AI experience as they ready themselves for independent research and faculty positions. Upon completing the program, our first cohort of Fellows have gone on to faculty positions at Penn State, Carnegie Melon, the University of Texas-Austin, and right here at Michigan, as well as careers at Ford Motor Company and General Motors.

Meet the current Fellows

Teaching Data Science to Faculty Researchers

The Introduction to Data Science for Biomedical Researchers Bootcamp is the first bootcamp that MIDAS offers to help researchers adopt data science methods. The intensive training started the participants on the journey to learn machine learning and its clinical applications, incorporate data science methods in their research programs and grant proposals. Following the success of this event, MIDAS is now developing similar bootcamps for researchers in other domains.

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