Shiyan Zhang

I have a bachelor’s degree of Geographic Information Science from BUCEA, China, and I learned the basic theories of GIS and remote sensing as an undergraduate. Having spent a lot of time on a variety of software usage practices, such as ArcMap, ENVI and Erdas Trimble, I gained a solid foundation in GIS and a clear understanding of the science.

When I graduated from the college, I went to George Washington University to continue my graduate study in order to bring my understanding of GIS to a higher level. The graduate study at GWU differed from my undergraduate work in the fact that generating the research ideas and improving data analytic skill with technologies other than GIS. Here I not only learned techniques like Python programming, database SQL, as well as spatial statistics and spatial analysis, and but also I got the chance to apply what I learned in class to real-world issues, such as calculated accessibility in urban areas and OLS regression.

After graduation, I worked three years and participated in several research such as Modeling and Analysis of a Potential Dam Failure for the Péligre Dam in Haiti, and Roads and the geography of economic activities in Mexico, where I mainly provided technical support.

My current research interests are spatial statistical analysis, machine learning, and deep learning and using the above to address the challenges in the characterization and predictability of environmental hazards. I will focus on developing and applying advanced spatial data science methods to air pollution-related studies, such as improving the accuracy of air quality prediction and how the changes in air quality affect human activity.