Biography
Zexuan Xu is a hydrology postdoctoral researcher at Earth and Environment Science Area, Lawrence Berkeley National Laboratory (LBNL), and Data Science Fellow at Berkeley Institute for Data Science and U.S./China Clean Energy Research Center for Water-Energy Technologies (CERC-WET) at University of California, Berkeley. Before joining Berkeley Lab, Zexuan received his PhD degree in hydrology and groundwater modeling at Florida State University (FSU). His PhD dissertation is “Data analysis and numerical modeling of seawater intrusion through conduit network in a coastal karst aquifer.”
Zexuan is currently working on the integrated hydrology and reactive transport model in East River, Colorado, supported by Watershed Function SFA project. He use reactive transport modeling approach to study the concentration-discharge relationship and understand the interaction of hydrology and subsurface mineral compositions in geological layers. His postdoc research was also supported by LBNL LDRD “Modeling the Earth’s hydrological cycle from watershed to global scales,” to investigate the responses of water resources, such as streamflow and groundwater levels, to atmosphere processes, land cover changes and human factors.
As the Data Science Fellow at BIDS and CERC-WET, Zexuan is working on the large-scale data analysis and variable-resolution climate modeling to study water resources in a changing climate. He develop the reproducible data tool for climate models, and would like to bring the cutting-edge methodologies and data analysis tools to problems in the earth and environmental sciences. He is also interested in the data-driven technique in climate and hydrology predictive model using machine learning algorithms.
As the Data Science Fellow at BIDS and CERC-WET, Zexuan is working on the large-scale data analysis and variable-resolution climate modeling to study water resources in a changing climate. He develop the reproducible data tool for climate models, and would like to bring the cutting-edge methodologies and data analysis tools to problems in the earth and environmental sciences. He is also interested in the data-driven technique in climate and hydrology predictive model using machine learning algorithms.