MIDAS coordinates efforts to bring attention to important research topics that cut across traditional disciplines, fosters interaction between theorists and application scientists, enables innovative ideas and new collaboration, and elevates the quality of data science research across U-M campuses. We particularly encourage researcher-initiated working groups and workshops to:
- Identify novel research themes where U-M researchers have the potential of making significant scientific contribution and societal impact;
- Develop research ideas that will become major grant proposals;
- Build interdisciplinary teams.
For researcher-initiated working groups, MIDAS can help coordinate the activities and connect researchers from all U-M units with diverse backgrounds and expertise.
To join a current working group and submit an idea for a new group, please email email@example.com.
Current working groups:
- Selection Bias and Missing Data in COVID-19 Population Studies. This group focuses on core information for data collection; datasets on sensitivity and specificity; methodology and tools. (View Working Group page)
- Learning Environment in the Time of COVID-19. This group discusses data science methods to design an inclusive, innovative, and resilient university from three angles: how to plan for the fall and the next few months, building adaptive capacity for the next year, and improving the university for the long term using evidence-driven strategies.
- Informing randomized, controlled trials with observational data (coming soon).
Past working groups:
- Data Integration. Challenges such as idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues.
- Data Science for Music. This group attracted researchers with diverse backgrounds to discuss research at the intersection of data science and music.
- Mobile Sensor Analytics. Discussions on theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.
- Teaching Data Science. Discussions on issues such as: How do we teach data science to students with various levels of preparation? How do we build data science modules to incorporate into existing domain science courses? How do we raise awareness of ethics and social responsibility in data science teaching? How do we teach data science to independent researchers?
- Trustworthy Data Science. Data security, privacy, data fairness, validity, and sensible applications to policy.
Past working groups linked with funding opportunities:
- Foundation Funding and Social Science
- NSF Big Data Spokes
- NSF BIGDATA
- NSF Resource Implementations for Data Intensive Research in the Social, Behavioral and Economic Sciences
- NSF Secure and Trustworthy Cyberspace