The Michigan Institute for Data Science (MIDAS) supports the ethical and responsible use of Generative Artificial Intelligence (generative AI) to accelerate disciplinary and interdisciplinary research. We enable new research through research incubation events, help researchers build generative AI skills through tutorials, and provide resources for U-M researchers. We also develop research collaboration with peer institutions, and coalition with industry and public sector organizations to promote the ethical and responsible use of generative AI in organizational operations and decision making.

Faculty or units are welcome to reach out to us (midas-research@umich.edu) to develop collaboration.

What’s new

University of Michigan has become the first major university to offer a custom AI platform for its entire community, offering a suite of custom generative AI tools in collaboration with Microsoft.

  • U-M GPT, a tool that provides access to popular hosted AI models such as Azure OpenAI and U-M hosted open-source large language models.
  • U-M Maizey, a tool that allows U-M faculty, staff, and students to enrich their GenAI experience based on a custom dataset they provide.
  • U-M GPT Toolkit, designed for those who require full control over their AI environments and models. 

Guides & Resources

Using Generative AI for Research, a Quick User’s Guide” Start from this guide on how generative AI can be used in multiple aspects of your research, based on published guidelines by journals, funding agencies and professional societies, as well as our assessment of generative AI’s benefits and risks. For information on the instructional use of generative AI, please see U-M guidelines: Instruction in an AI-Augmented World.

Generative AI Resource Hub. This is a curated collection of generative AI models, research papers, and other resources to help researchers start integrating generative AI into their work.

Past Events

August 2023: Generative AI Coast-to-Coast seminar and research discussion series. The Generative AI Coast-to-Coast Webinars brought together eight prominent researchers from six peer data science institutions to discuss generative AI in research; themes included ‘Generative AI in Healthcare and Public Health,’ ‘Generative AI in the Lab,’ ‘A Conversation on Policy, Ethics, and Generative AI,’ and ‘An Under the Hood Look at Generative AI: Potentials and Pitfalls.’ All webinar recordings are available on our YouTube channel.

Participating institutions include Johns Hopkins University, the Ohio State University, Rice University, the International Computer Science Institute, an affiliated institute of the University of California, Berkeley, and the University of Washington.

September 15, 2023: Generative AI Diffusion Models for Scientific Machine Learning. Recently, diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image generation, audio synthesis, inverse problem solving, and many scientific disciplines. However, despite their impressive results, they also encounter numerous challenges and constraints that inhibit their practical implementation in many scientific pursuits. Therefore, this MIDAS symposium will serve as a timely platform where experts and researchers from both methodology and application research fields will explore the latest progress and developments in generative AI and diffusion models, and delve into the application of these models in scientific and medical fields, which will become a prime venue for idea exchange and fostering research partnerships in this emerging field. Co-organized by the Department of Electrical and Computer Engineering.

October and November 2023: Transforming Your Research with Generative AI – Tutorial Series. MIDAS and the Michigan AI Laboratory jointly offered a training series to researchers across research fields as they start incorporating generative AI in their research. Each session consisted of lectures, demonstrations, and hands-on tutorials for using generative AI in research as it related to a specific theme.