Stanford Doerr School of Sustainability
Showing 51-60 of 102 Results
-
Fa Li
Postdoctoral Scholar, Earth System Science
BioMy research combines advanced data-driven approaches (e.g., GeoAI and causality inference), process-based terrestrial biosphere/Earth system models, and big datasets and techniques of remote sensing, in-situ measurements, geographical information science (GIS), and high-performance computing, to investigate critical processes related to natural greenhouse gas emissions (e.g., CO2 and CH4) and nature-based climate solution, wildfire-human-climate interactions, human-environment interactions, and biosphere-atmosphere interactions of carbon-water-energy fluxes affecting climate change.
-
Mengze Li
Postdoctoral Scholar, Earth System Science
Current Research and Scholarly Interestsatmospheric gases: trends and emissions, such as methane, volatile organic compounds.
atmospheric observations: ground, airborne, satellite remote sensing.
atmospheric measurement techniques.
atmospheric modeling.
indoor air chemistry and human emissions.
climate change. -
Zhi Li
Postdoctoral Scholar, Earth System Science
BioZhi “Allen” Li is the Stanford Doerr School of Sustainability Dean’s Postdoc Fellow. He studies surface water across scales, both spatially (local, continental, and global) and temporally (Hydrology, Hydrometeorology, and Hydroclimatology). His research focuses on flood prediction and monitoring by leveraging Remote Sensing platforms and Hydrologic-Hydraulic coupled models. He devotes himself to improving flood monitoring tools to deliver accurate and timely information for the community, especially under-represented communities.
-
Laura Mansfield
Postdoctoral Scholar, Earth System Science
BioI am interested in how machine learning and Bayesian statistics can assist our understanding and prediction of the climate and weather. My current research focuses on improving gravity wave parameterizations in atmospheric circulation models, which are necessary to capture the subgrid-scale gravity waves that influence the middle atmosphere dynamics. Machine learning can be used to either improve existing physics-based parameterizations, i.e. through calibration, or to replace these entirely with novel machine learning alternatives. I work on both of these approaches and am particularly interested in exploring uncertainties arising from parameterizations.
Previously, I completed my PhD at the University of Reading, which focused on emulating climate models to estimate the surface temperature response to changes in anthropogenic forcings, including both long-lived greenhouse gases and short-lived aerosol pollutants. Prior to this, I completed the Mathematics of Planet Earth MRes at University of Reading, after coming from an undergraduate degree in Physics at Imperial College London. Outside of work, my interests include cycling, running and being outdoors in California. -
Cheng Mei
Postdoctoral Scholar, Geophysics
BioI am currently a postdoc scholar at Department of Geophysics. My research covers earthquake mechanics, numerical modeling, and rock friction and deformation.