معرفی
Dorit Hammerling is an Associate Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. She holds a PhD in Environmental Engineering from the University of Michigan, along with an MA in Statistics and an MS in Civil Engineering from Michigan Technological University. Her research focuses on spatial statistics, environmental and computational statistics, and applications in climate science, remote sensing, and oil and gas emissions monitoring. She currently leads projects on methane monitoring technologies and climate model consistency testing.
Hammerling's research interests include developing statistical methods for large spatial datasets, analyzing climate model outputs, and optimizing sensor placement for emissions detection. Her work bridges environmental engineering, data science, and computational methods, with applications to energy systems and atmospheric processes.
Her recent articles emphasize advancements in methane emission localization using physics-informed neural networks, ensemble consistency testing for climate models, and benchmarking climate data compression techniques. These studies highlight her interdisciplinary approach to solving complex environmental and computational challenges.
Hammerling teaches graduate-level courses such as Math 560 (Statistical Learning Methods) and Math 482/582 (Statistics Practicum), integrating practical data analysis with theoretical foundations. She collaborates with interdisciplinary teams to address real-world issues in energy and environmental systems, emphasizing reproducibility and computational efficiency in scientific workflows.
