Research interests

My research is broadly focused on utilizing large-scale geodetic data to extract meaningful insights into the behaviors and properties of physical systems. Generally, the spatiotemporal signals of interest encoded in these data are confounded by noise, external forcing effects, data corruption, etc. My research involves combining the power of modern machine learning algorithms and physical modeling to disentangle the primary signals of interest in geodetic data and associate those signals with physically-relevant processes. Additionally, I am interested in scalable uncertainty quantification for large-scale datasets and complex models in order to guide research questions on optimal data acquisition and model development.

I am currently developing and applying these tools to geodetic data over fast-flowing glaciers and ice streams in Greenland and Antarctica in order to understand the dynamic response of ice sheets to climate change. Similarly, I am studying the evolution of glacier flow and mass balance in High Mountain Asia in order to study how changing atmospheric conditions modulate hazard risk and glacial water resources. Overall, my research aims to constrain future projections of the cryosphere using large-scale datasets coupled with scientific machine learning.

Education

2011 - 2017  Ph.D., Geophysics

                     California Institute of Technology

2008 - 2010 M.S., Aerospace Engineering

                    University of Texas at Austin

2004 - 2008 B.S., Aerospace Engineering

                    University of Texas at Austin

Employment History

2022 - present  Associate Professor

                         Zhejiang University

2019 - 2021  Research Scientist

                     Massachusetts Institute of Technology

2016 - 2019  Signal Analysis Engineer

                     Jet Propulsion Laboratory, NASA

2009 - 2011  Engineering Summer Intern

                     Jet Propulsion Laboratory, NASA