Ying Sun, PhD
Dr. Sun is an Assistant Professor at School of Integrative Plant Science, Soil and Crop Sciences Section at Cornell. She is also a Faculty Fellow for Atkinson Center for Sustainable Future. She received her Ph.D. in Climate Science from the University of Texas at Austin in 2013, and her B.S. in GIS and Remote Sensing from Beijing Normal University in China in 2008. Her research seeks to understand how plant eco-physiological processes govern the interaction and feedbacks between terrestrial ecosystems (both natural and managed) and climate.
Meijian Yang, PhD
Dr. Yang's current research mainly focuses on disentangling the compound effect of Genetics x Environment x Management (GxExM) on agricultural productivity from regional to global using process-based crop model and earth system model, and further investigating the agricultural feedback to climate change. My research aims at facilitating a more sustainable agricultural system to alleviate both food insecurity and climate shocks.
Mr. Wen received his B.E. degree in Hydrology and Hydraulic Engineering from Tsinghua University in 2017, and is currently pursuing a Ph.D. degree at Cornell. His research focuses on applying Solar-Induced chlorophyll Fluorescence (SIF) satellite observations to study terrestrial ecosystem dynamics, climate-carbon feedbacks, and crop yield prediction.
Miss Jiameng's research interests include: global carbon cycle, terrestrial water dynamic; urban climate,. I use satellite remote sensing data, reanalysis data, as well as terrestrial biosphere models to advance solutions to important environment questions. My current project is to use multiple photosynthesis tracers to advance our predictive understanding of GPP using NCAR CLM5.
Mr. Holmes's research focuses on assimilating multiple satellite based observations into NCAR’s Community Land Model (CLM5) with the goal of constraining simulated above ground processes and improving simulated carbon dynamics. My research interests include Earth System Science and modeling, the use of remote sensing and machine learning for the classification and characterization of land surface phenomena and processes across space and time, the dynamic nature of socio-ecological systems and how various management paradigms affect ecosystem structure and function.