Understanding the controlling factors behind the chemical composition of the Earth’s atmosphere is a critical step toward addressing the modern environmental challenges of air pollution and climate change. Traditional methods interrogating theoretical predictions with observations have been highly successful in addressing these challenges, particularly in light of the recent immense growth of data availability in environmental systems. However, there are still gaps in our scientific knowledge due to limitations in modern scientific techniques (e.g., theoretical frameworks, observational systems, and computational power). Data-driven methods from the data science and artificial intelligence literature, when informed and guided by scientific understanding, present a valuable tool in addressing these knowledge gaps. In this seminar, I will present results from recent work using a variety of data science and A.I. methods to better constrain modern understanding of atmospheric composition and the climate system.
Speaker Bio
Sam Silva is an assistant professor of Earth Sciences, Civil and Environmental Engineering, and Population and Public Health Sciences at the University of Southern California. Prior to his current position, he worked as a research data scientist at the Pacific Northwest National Laboratory, a U.S. Department of Energy research laboratory. He received a Ph.D. in Environmental Engineering and Computation from the Massachusetts Institute of Technology, and an M.S. in Atmospheric Science and B.S. in Physics from the University of Arizona. His research is focused on air pollution and climate change, with particular interest in the convergence of traditional computational methods with modern data science and artificial intelligence techniques.
