What Can We Learn From Big Water Quality Data? Matthew Cohen, UF

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In this era of big data and machine learning, water science continues to grapple with how best to leverage new tools for better understanding and improved decision making. In this talk, I will describe the status of water data in Florida and beyond, advocate for ways to lower barriers to data access, and describe new data products that curate the vast repositories of data that exist across data providers. Using a recent example of benchmark data set creation, I will explore some of the foundational questions that big data can help answer, and also some potential pitfalls from overemphasizing prediction over understanding. The talk will also explore the landscape of collaborations in water scholarship, including big data initiatives across UF campus, catalyzed by the UF Water Institute.

Matt Cohen is the Carl Swisher Eminent Scholar in Water Resources, and Director of the Water Institute at the University of Florida. His research focuses on wetlands, rivers, and aquifers. He received a BS from Swarthmore College (PA) in environmental engineering and a PhD from the University of Florida College of Engineering in systems ecology. His scholarly passion is ecohydrology, which explores the critical interface of ecosystems and water. His research has focused on Florida, exploring the impacts of land management on water quality and quantity, the health and function of wetlands, and changes in the energy-basis of river ecosystems. His most recent work focuses on the restoration of Florida’s wet landscapes, deep-learning from patterns of high frequency water quality variation, and the impacts of nutrient loading in flowing waters.