Dr. Hammerling obtained a M.A. and PhD (2012) from the University of Michigan in Statistics and Engineering, followed by a post-doctoral fellowship at the Statistical Applied Mathematical Sciences Institute in the program for Statistical Inference for massive data. She then joined the National Center for Atmospheric Research in the Institute for Mathematics Applied to the Geosciences and later worked in the Machine Learning division before becoming an Associate Professor in Applied Mathematics and Statistics at the Colorado School of Mines in January 2019. She received the Early Investigator Award from the American Statistical Association, Section on Statistics and the Environment, in 2018, and Outstanding Associate Professor of the College, in 2024.
Talk: Methane emission detection and localization on oil and gas facilities using continuous monitoring sensors
Abstract: Methane, the main component of natural gas, is the second-largest contributor to climate change after carbon dioxide. Methane has a higher heat-trapping potential but shorter lifetime than carbon dioxide, and therefore, rapid reduction of methane emission can have quick and large climate change mitigation impacts. Reducing emissions from oil and gas production facilities, turns out to be a particularly promising avenue due to technological advances in continuous emission monitoring technology. We present a Bayesian framework for quick emission detection and localization using continuous methane concentration data measured by multiple monitoring sensors on oil and gas production facilities, demonstrate its effectiveness under real-world conditions and discuss ideas for future directions.