MIDAS organizes research working groups for faculty and research staff based on research themes and funding opportunities. These working groups bring attention to important research topics that cut across traditional disciplines, foster interaction between theorists and application scientists, enable innovative ideas and new collaboration, and elevate the quality of data science research across U-M campuses. The groups meet regularly (based on research themes) or one time (based on funding opportunities). At the meetings, researchers present their ideas or ongoing projects to seek feedback and/or collaboration. We also compile research profiles for each group, when the timing is right, that are used to communicate with potential academic and industry partners.
We welcome suggestions of themes for new groups. To propose a new group or to join any existing group, please contact Jing Liu (email@example.com; 734-764-2750).
Themed Research Working Groups:
- Data Integration. Data integration is an essential component of data science research in almost all research areas that use heterogeneous data varying in format, dimensionality, quality and granularity. The examples are endless: multi-omics data integration is increasingly critical in biological research; clinical research benefits greatly from the integration of patient longitudinal data, lab data, sensor data and other types of diagnosis and self-report; environmental monitoring often needs the integration of statistical data, image data and geospatial data; social science research, including education, political science and economics, increasingly integrates social media and other web-based data with traditional survey data… All the applications encounter similar data science challenges, including idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues. Our working group creates an interdisciplinary forum that will foster new ideas and collaboration.
- Data Science for Music. In 2018, MIDAS launched the Data Science for Music research hub and funded four interdisciplinary research projects. With this hub as the first step, MIDAS hopes to help U-M scientists position themselves at the national forefront in this nascent research area. The Data Science for Music working group is our next step to attract researchers with diverse background who would like to venture into this research area. We define “data science for music” broadly, including computer music; computational music theory; Big Data analytics in music education, audience engagement and marketing; music in healthcare, advocacy and other settings; and any other research at the intersection of data science and music. Our working group hosts regular meetings as an interdisciplinary forum to foster new ideas and collaboration.
- Mobile Sensor Analytics. Mobile sensors are taking on an increasing presence in our lives. Wearable devices allow for physiological and cognitive monitoring, and behavior modeling for health maintenance, exercise, sports, and entertainment. Sensors in vehicles measure vehicle kinematics, record driver behavior, and increase perimeter awareness. Mobile sensors are becoming essential in areas such as environmental monitoring and epidemiological tracking. There are significant data science opportunities for 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. Our working group welcomes researchers with interest in mobile sensor analytics in any scientific domain, including but not limited to health, transportation, smart cities, ecology and the environment.
- Teaching Data Science. As we incorporate data science into almost every level of teaching, many issues need to be thoroughly thought out: How do we teach data science to students with various levels of preparation, from those with little quantitative training to STEM students? 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, including faculty, who want to build data science into their research? What teaching resources are available at UM? Our working group creates an interdisciplinary platform that will foster new ideas and collaborations in the development of data science teaching methods and materials.
- Trustworthy Data Science. This broad theme includes research and its application on data security, privacy, data fairness, validity, and sensible applications to policy. Such topics are essential in data science methodology and tools development, and in many research areas including healthcare, education, business and finance, sustainability, and social sciences. Our working group welcomes methodologists as well as researchers in any research area who take these issues into consideration. We hope to create an interdisciplinary forum that will foster innovative ideas and new collaboration.
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