A new project led by CEE assistant professor Christopher Tessum will focus on improving efficiency of artificial intelligence (AI) used in air-pollution modeling. The project, funded through the NSF’s Collaborations in Artificial Intelligence and Geosciences (CAIG) program, was one of twenty-five projects selected as part of a $20 million investment encouraging AI in geoscience research.
CAIG supports the development of advanced AI methods for geosciences research, as well as associated educational and technical efforts to build capacity for adoption of AI-driven approaches by geoscientists. Projects selected for the new investment emphasize transdisciplinary research among geoscientists, computer scientists, mathematicians and others to drive transformative discoveries, innovations and solutions.
Tessum’s project will use machine learning to simplify components of current air-pollution models. Most geoscientific models are computationally intensive and therefore unusable for scientists without access to high-performance computing clusters. Simpler components will reduce the necessary computing power and provide wide-spread access to AI for geoscientists in all sectors, encouraging the use of AI models in geoscience research to expedite growth of knowledge and scientific breakthroughs.
Tessum joined the CEE department as an assistant professor in January 2020. His research focuses on modeling air pollution and its health impacts, quantifying inequities in the distribution of those impacts, and proposing and testing solutions. He studies the relationships between emissions, the human activities that cause them, and the resulting health impacts. He additionally develops modeling capabilities to enable these types of analyses.