Research

The Michigan Institute for Data & AI in Society (MIDAS) advances cross-cutting research in data science theory, methodology and applications.

Research Services

AI Sandbox

The AI Sandbox is an approachable space for researchers across disciplines to explore and develop AI skills.

Explore the Sandbox

AI in Research Handbook

A practical, open-access guide for researchers who want to use AI effectively in their work.

View the Handbook

AI Consultation

MIDAS expanded its AI Consultation program to support a wide range of faculty across campus in applying machine learning and (generative) AI to real-world research challenges.

Request a Consultation

Proposal Support

MIDAS offers comprehensive assistance to U-M researchers, including red team reviews, help drafting Letters of Support, and connections to data science collaborators and campus resources.

Submit this Google Form to access boilerplate language about MIDAS facilities and resources.

Request Proposal Support

Research Funding

MIDAS offers funding opportunities for students, researchers, and faculty, including:

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Propelling Original Data Science (PODS) Program

Since 2016, MIDAS has been offering Propelling Original Data Science (PODS) grants to U-M faculty to enable groundbreaking disciplinary and interdisciplinary research through data science and AI.

Learn About PODS
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Center for Data-Driven Drug Development and Treatment Assessment (DATA)

Funding for computational methods, data science solutions, artificial intelligence (AI) and machine learning (ML) techniques in drug design, drug repositioning, drug treatment assessment, patient phenotyping, and quantitative pharmacovigilance.

Learn About DATA
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MIDAS Funded Research

Explore how MIDAS-funded research is advancing the frontiers of data science and AI.

View Projects

Research Coordination

MIDAS provides resources to support projects through the entire life cycle of research, from ideation and skills training to publishing.

Faculty Research Connection Series

Each month, MIDAS hosts a gathering of faculty affiliates around a particular domain or theme to explore research questions or discuss possible grant proposals. Sessions include opportunities to meet and find collaborators and network.

View the Series

Faculty-Student Research Connection

Many U-M faculty members seek students with data science and AI skills for research assistance. Many U-M students in data science and AI programs and student organizations (together forming the MIDAS Student Organizations Council and representing more than 1100 graduate and undergraduate students) are eager to be involved in faculty research projects as individuals or as small student teams.

Get Connected

AI & Data Resources

Clinical Trial Randomization Tool

Streamline your clinical trial randomization with the Clinical Trial Randomization Tool, which helps researchers assign participants to treatment groups one at a time while maintaining balance across key covariates, even with small sample sizes.

Use the Tool

Reproducibility Hub

MIDAS promotes reproducible data science together with researchers in our community through raising awareness, celebrating best practices, enabling the scholarly investigation of reproducible research, and developing tools that can be widely adopted.

Explore the Hub

Funding Trends Discussion Series

Explore sessions from the MIDAS Funding Trends Discussion Series, where funders, campus leaders, and researchers shared insights on emerging funding priorities and strategic approaches. Although the series is no longer active, these recordings remain a valuable resource for understanding the evolving research funding landscape.

View the Series

Datasets

MIDAS helps U-M researchers develop new datasets and make datasets machine learning ready through training, as well as funding opportunities. Examples of datasets that have been supported through MIDAS grants (e.g., the Propelling Original Data Science program) or that have been developed by MIDAS-affiliated faculty, postdocs, and other researchers are highlighted below.

Learn About Datasets