Theme 1: Global Warming and Hydro-climatic Extremes 

Global warming is altering the characteristics of extreme hydro-climatic events including heavy storms, droughts, heatwaves and wildfires. We aim to understand these changes both in the observational record and future climate model projections using advanced data analytic tools of machine learning and statistical analysis. A main science question in our group is how can we detect robust signals of change in extreme events that are an impact-relevant in order to inform climate change adaptation.

Theme 2: Hydrologic Remote Sensing 

How can we utilize remotely-sensed observations, specifically satellite-based estimates of precipitation, to inform engineering design and climate change studies? How can we improve the retrieval of precipitation estimates from satellites using advanced data-driven approaches? Our research in this theme is driven by these questions. 

Theme 3: Machine Learning and AI for Hydrologic Applications

How can we utilize remotely-sensed observations, specifically satellite-based estimates of precipitation, to inform engineering design and climate change studies? How can we improve the retrieval of precipitation estimates from satellites using advanced data-driven approaches? Our research in this theme is driven by these questions.