My research interests lie at the intersection of data science and geoscience. I am interested in deriving scientific insights by utilizing the tools of data science to tackle geoscience problems, particularly as applied to remote-sensing of the cryosphere. This includes automated processing pipelines of remote-sensing data products, such as automated feature detection on glaciers from a variety of satellite data sources. Furthermore, I’m interested in interpretable machine learning approaches in exploring complex dynamic systems.

We are at the cusp of a revolution in geoscience, and specifically glaciology, where the sheer volume of observational data will shift the current bottleneck in our understanding from data availability to processing ability and interpretability. The parallel advancements in computational tools, from probabilistic programming platforms to machine learning and big data in geosciences, provide the perfect opportunity to push the limits of our understanding of changes in the cryosphere.

I’m also interested in improving our understanding of glaciological processes contributing to sea level rise. To that end, quantifying mass balance of glaciers at a regional level and understanding glacier dynamics are of crucial importance. I believe interpretable machine learning approaches can also assist with an improved understanding of key, but difficult to model processes, such as marine ice cliff instability.