Emerging Pillar: Cultivating New Strengths
We support team activities in strategic research areas that have the potential to grow into pillars. The focus is on areas that are, or are expected to be, national priorities and / or U-M strengths, and can be significantly boosted with Data Science and AI. Naturally, activities in this pillar are exploratory, and a strong focus will emerge with time.
Want to participate in pillar activities or have questions? Please email firstname.lastname@example.org.
Data Science for Environmental Research
Overview: As research on the environment, climate change and sustainability becomes a national priority, an increasing number of U-M environmental scientists are embracing data science and AI methods. MIDAS has been building collaboration and support for these researchers and our concerted effort lays the foundation for our next pillar.
MIDAS is planning a training program for environmental scientists to help U-M faculty and research scientists adopt data science and AI techniques to environmental science research and integrate data science into their grant applications. The program will also help foster a U-M research community that will advance the application of data science and AI to research that encompasses environmental, climate, and earth sciences; as well as ecology. The full training program will consist of three phases: a half-day workshop, a week-long bootcamp, and two optional half-day follow-up sessions.
In addition, our Propelling Original Data Science (PODS) pilot funding program has been supporting a number of environmental research projects that use cutting-edge data science and AI methods, including:
- Improving the efficiency of energy grids
- Improving urban water quality
- Better machine learning models to monitor air quality
- Supporting decision making for navigating vital waterways in winter
- Identifying communities vulnerable to climate change
- Developing data systems of fish communities in the Great Lakes
- Detecting illicit wildlife trading
Who Will Benefit: All environmental scientists who want to explore data science and AI methods for their research, and methodologists who seek to collaborate with environmental scientists.
Coordinator: Jing Liu (Managing Director, MIDAS)