MIDAS Summer Academies
Our summer academies equip researchers with the essential skills required to apply advanced AI and data science techniques to their respective fields of study and integrate these methodologies into their grant proposals. The overarching objective of these academies is to foster a research community that can drive the advancement of data science applications in a wide range of research fields.
Curriculum Committee
Chris Brooks
Research Assistant Professor, School of Information Director, Learning Analytics and Research in the Office of Digital Education & Innovation
Elle O’Brien
Lecturer III; Research Investigator, School of Information
Kerby Shedden
Professor, Statistics, LSA, Biostatistics, School of Public HealthDirector of Consulting for Statistics, Computing, and Analytics Research (CSCAR)
Faculty Leads
Elle O’Brien
Lecturer III; Research Investigator, School of Information
Alex Rodriguez
Assistant Professor of Electrical Engineering and Computer Science, College of Engineering
Kerby Shedden
Professor, Statistics, LSA, Biostatistics, School of Public HealthDirector of Consulting for Statistics, Computing, and Analytics Research (CSCAR)
Testimonials
It was a digestible, yet detailed, introduction to ML/AI that is important and timely given trends in scientific data analysis. I was recently told by an NSF Program Officer that it would be a great benefit to include some form of AI in a proposal to align with current administration priorities. I would have been totally “up a creek without a paddle” without the Summer Academy.
— Matthew Holding, Research Investigator, Life Sciences Institute
Since completing the Summer Academy, I have seen clear and measurable progress in both my technical capacity and research progress.
— Anthony Pembere, African Faculty Fellow, 2025 Cohort
The summer academy has opened the door into the AI world and has made me understand the foundation model and frameworks of AI as applied to scientific research space.
— Joseph Osumeje (Ph.D.), Schmidt AI in Science African Faculty Fellow, and a Faculty at Department of Physics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
The Summer Academy was an eye-opening experience that highlighted the broad applicability of AI and machine-learning techniques to my field of research, Space Physics. It provided a valuable insight that enabled me to explore and critically assess a range of advanced methodologies that can be integrated into my research work, particularly for modeling and predicting key ionospheric parameters using ML-based approaches. The training significantly expanded my research perspective and methodological toolkit.
— Dr. Valence Habyarimana, Mbarara University of Science and Technology, Mbarara, Uganda; Schmidt African Faculty Fellow, University of Michigan
Questions
For questions about the summer academies, please reach out to Kelly Psilidis ([email protected]), MIDAS Faculty Training Program Manager.