
Inventing tomorrow: Dani Jones

Dani Jones is an associate research scientist at the Cooperative Institute for Great Lakes Research at the University of Michigan School for Environment and Sustainability (SEAS). We asked him to share more about his work in this brief Q&A.

What is your research focused on?
I use data science and machine learning to tackle environmental challenges in the Great Lakes region. At the Cooperative Institute for Great Lakes Research (CIGLR), I’m leading the creation of the Great Lakes Artificial Intelligence (AI) Laboratory, a collaborative space where experts from different disciplines work on advanced computational methods. Our research spans physical limnology, weather forecasting, water cycle predictions and ecology, while also improving observing systems. By combining large datasets with scientific models and interdisciplinary expertise, we aim to deepen our understanding of Great Lakes variability across a wide range of spatial and temporal scales.
What is the impact of this research?
This research plays a role in helping communities in the Great Lakes region adapt to the dynamic, changing nature of the lakes. The Great Lakes are not only a crucial freshwater source but also an economic hub, making accurate environmental predictions essential. By improving water-level forecasting, we provide better water-level predictions, helping protect communities, infrastructure, agriculture and industry. Enhanced observing systems with optimized sensor placement also strengthen environmental monitoring, delivering the data needed for adaptation and mitigation strategies. While my background in physical oceanography informs my approach, my focus is on the unique challenges of the Great Lakes. Through regional and international partnerships, the Great Lakes AI Lab fosters collaboration to develop innovative, data-driven solutions for a more sustainable future.
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