About MIDAS

Our Goals

1. Research: Lead strategic thrusts in Data Science and Artificial Intelligence research; enhance institutional research capacity by enabling collaborative grants, providing research training, and building research resources; facilitate the development of cutting-edge methods and the application of these methods to important problems in all disciplines. 
2. Community: Grow a world-class multi-disciplinary Data Science and Artificial Intelligence research community at U-M, promoting diversity, ensuring inclusion, and providing multiple entry points. 
3. Impact: Be the nexus for Data Science and Artificial Intelligence within and beyond U-M, build coalitions with academia, industry, government, and the community, and promote positive social change.

Our Community and Programs

MIDAS leads, enables and facilitates data science and AI research through a number of approaches. 

1. We develop guidance and resources, together with our faculty, for reproducible, responsible and ethical Data Science and AI. 
2. We help faculty secure major grants, especially collaborative grants, through ideation, team building and proposal review. We also support faculty’s education and outreach efforts that are an integral part of their grants.
3. We provide workshops and bootcamps to introduce data science and AI, as well as cutting-edge methods, to scientists in a wide range of research fields. 
4. We develop and manage research resources to enhance the U-M research capacity, including pilot funding, dataset access and industry collaborations.
5. We catalyze the transformative use of Data Science and AI for disciplinary and interdisciplinary research in collaboration with academic departments and other research institutes on campus.
6. We organized a large number of research events, including a weekly seminar series, the annual symposium, and other events often in collaboration with other units on campus.
7. We connect students to faculty research projects, offer data challenges and real-world projects to students to enhance their research training.

>420 affiliate faculty members come to MIDAS from all schools and colleges at U-M Ann Arbor campus, and from U-M Dearborn and Flint campuses. Their expertise encompasses theoretical foundations of data science, a wide range of data science and artificial intelligence methodology, and its applications in almost all research areas at U-M. Our Michigan Data Science Fellows program forms the core of a data science and AI postdoc community on campus. Our community also includes staff scientists and >1000 graduate and undergraduate students. MIDAS runs a Graduate Data Science Certificate Program and is a partner in the U-M Master’s in Data Science program and undergraduate data science programs. The Data Science Student Leadership Board, supervised by MIDAS staff, plans many DEI and academic enrichment events.

MIDAS promotes a diverse and inclusive data science community by giving the spotlight to women and underrepresented minorities, and organizing training and social events specifically for these underrepresented groups. These events include the Women+ Data Science career and research series, a Diversity in Data Science summer camp for underserved students, and a nationwide Data Science Consortium focusing on outstanding women and under-represented minority trainees.

MIDAS works with a large number of industry, academia, government and non-profit organizations to ensure that Data Science research is enabled by real-world data and inspired by real-world challenges, and that research outcomes are translated into products, services and policies for positive social change. We also organize an annual Data for Public Good symposium together with campus collaborators. Our External Partnership program offers comprehensive support for joint research and talent recruitment. MIDAS is a member of the NSF Midwest Big Data Hub and provides leadership in several focus areas. We also work closely with academic data science organizations to foster a broader and collaborative research community and to maximize synergistic impact.