Guofeng Cao awarded NSF grant

Anonymous — Thu, 10 Dec 2020 21:54:50 +0000

Guofeng Cao awarded NSF grant Anonymous (not verified) Thu, 12/10/2020 - 14:54

Professor Guofeng Cao

Dr. Guofeng Cao received a $265,058 funding support from National Science Foundation for the project proposal "Deep Learning in Geospatial Uncertainty Modeling''. With this support, Guofeng will develop new deep learning-based spatial statistical framework to address long-standing problems in geospatial analysis, including complex geospatial patterns, geospatial heterogeneity and geospatial uncertainty. This project will offer novel solutions to fundamental analysis, modeling, and integration problems involving geospatial data, and advance the understanding of the nature of geospatial uncertainty. This project will enhance the proper and cost-effective utilization of geospatial data, and will have broader impacts on disciplines that geospatial data are involved. Furthermore, with a public outreach on uncertainty-aware spatial thinking, this project will advance the public good by increasing the public awareness of the geospatial uncertainty and critical map reading and usage. The performances of the developed methods will be evaluated in two domain applications: spatiotemporal disease mapping in public health and modeling uncertainty of land cover changes and the impact on atmospheric models.

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Introducing Guofeng Cao

Anonymous — Thu, 10 Dec 2020 21:51:08 +0000

Introducing Guofeng Cao Anonymous (not verified) Thu, 12/10/2020 - 14:51

Guofeng Cao

I am a geographic information scientist with broad training in geography, statistics, computer science and earth sciences. Prior to joining ��Ƶ, I spent several years teaching in the Department of Geosciences at Texas Tech University where I also served as the director of the Center for Geospatial Technology. I have a PhD in Geography and a Master's degree in Applied Probability and Statistics from the University of California, Santa Barbara. I did my postdoc training at the University of Illinois Urbana-Champaign (Geography Department and National Center for Supercomputing Applications). I did my undergraduate (Earth Sciences and Computer Science) in Zhejiang University back in China, and before moving back to academia I also spent several years in industry.

My research is characterized by an interdisciplinary perspective on geographic information science driven by the advances of spatial Big Data (e.g., social media and remote sensing), machine learning/artificial intelligence, and computational sciences. The overarching goal is deep learning of heterogeneous geographic information to support uncertainty-aware geographic knowledge discovery and decision making. Particularly, I focus on the development of statistical/machine learning and computational methodologies to integrate heterogeneous sources of geographic information for complex spatiotemporal patterns. I am particularly interested in characterizing and modeling geospatial biases and uncertainty of geographic information and the associated impacts in scientific applications and practical decision making. I also develop methods and tools to address the computing challenges that arise when the data scales and computation complexity are not manageable with regular computers. I work closely with domain scientists to build geospatial cyberinfrastructure to tackle domain challenges, with a particular focus on natural hazards, environmental sciences, public health and global changes. My research has been supported by several funding agencies, including NSF, USGS, NIST, USAID, USDA and NIH.

I am very fortunate and excited to join the family of the ��Ƶ Geography, the world-renowned geography program, and very excited to explore collaborative opportunities with colleagues within the department and across the ��Ƶ campus. After spending several years in the Texas high plains, I am also very excited about the wonderful geography, landscape and weather variations in the Rockies.

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