Research
My research interests focus on developing computationally efficient methods to estimate greenhouse gas (GHG) emissions using machine learning and stochastic methods. Next generation dense observing systems are providing unprecedented coverage of GHGs. This will help us understand why the global concentrations of GHGs are rising and the role of point sources. However, conventional atmospheric transport models present computational challenges which limit our understanding of point sources and global carbon cycle. We propose machine learning and stochastic based methods to address these computational limitations.