Hiring: We are looking for a Postdoctoral Researcher to join our group at Clark Center for Geospatial Analytics. See details here.

I am an Associate Professor in the Graduate School of Geography at Clark University and the Director of Clark Center for Geospatial Analytics. My research interest lies at the intersection of GISciences and geography to use observations and analytical methods, in particular machine learning (ML) techniques, to better understand the changing Earth system.

Recently, in my group we have been focused on developing and evaluating Foundation Models for Earth Observations. You can check the latest updates of this work here.

My three main areas of research are:

  • Adapting Machine Learning Techniques to Earth Observation Data ()
  • Characterizing Uncertainties in ML Model Predictions
  • Applications of SAR for Soil Moisture and Vegetation Properties Retrieval

Before joining Clark University in Jan. 2023, I was the Chief Data Scientist and Executive Director at Radiant Earth Foundation, an organization with the vision to empower organizations and individuals globally with open ML and EO data, standards and tools to address the world’s most critical international development challenges. I established and led the development of Radiant MLHub - the open-access repository for geospatial training data and models at Radiant Earth.

Before joining Radiant Earth Foundation in 2017, I spent one year as a Postdoctoral Research Scientist at Columbia University’s Earth and Environmental Engineering Department working with Pierre Gentine. My research was focused on improving our understanding of the heterogeneous processes linking the water, carbon and energy cycles. In particular, I developed new retrieval algorithms from remote sensing observations for different variables of the Water and Carbon cycles and using the remote sensing estimates to characterize the dynamic feedback between terrestrial ecosystem and atmosphere.

Before joining Columbia University, I was a Postdoctoral Research Associate in the Department of Civil and Environmental Engineering at MIT working with Dara Entekhabi in Parsons Laboratory for Environmental Science and Engineering. At MIT, I developed a new polarimetric retrieval algorithm for NASA JPL’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission to estimate soil and vegetation parameters from P-band Synthetic Aperture Radar (SAR) observations.

I received my Ph.D. in Civil and Environmental Engineering from MIT in 2014 (supervisors: Prof. Dara Entekhabi, Prof. Dennis McLaughlin). My PhD dissertation was focused on quantification of uncertainty in remotely-sensed precipitation estimates. I developed an ensemble-based framework to characterize the uncertainty in precipitation estimates using historical errors. This framework generates realistic spatial (2D) replicates of rainfall that can be used to propagate the uncertainty into ecohydrological and meteorological models, especially those used in Data Assimilation.