Feeding the growing global population is challenging, especially when there are increasing competitions for land and water to maintain other essential ecosystem services. My research thus integrates ecology, computational modeling, remote sensing, and machine learning approaches to advance the science that guides sustainable agricultural management, and to develop tools that can help farmers and regulators apply this science more effectively.
With the rapid progress in earth observatory power and a range of modern tools, I'm particularly interested: (i) mapping agriculture features using high-resolution satellite imagery; (ii) forecasting crop yields for a range of applications; (iii) integrating crop models with remote sensing for precision N management; (iv) understanding the impacts of climate change on agroecosystem.
Prior to this position, I worked as the Lead Crop Scientist at Atlas AI P.B.C., Palo Alto, CA, and as a Postdoc at Stanford University. I received my B.S. degree from Peking University in 2011 and Ph.D. degree from Purdue University in 2016.