Liang Zhao, Assistant Professor, Information Sciences and Technology, is set to receive $102,873 from the National Science Foundation as part of a CAREER award for a project examining spatial network deep generative modeling, transformation, and interpretation.
This project will focus on developing a transformative framework for spatial network generative modeling, which can automatically learn the underlying complex generation process from massive spatial network datasets.
The project will generalize existing generative models of spatial networks into deep and expressive architectures. The developed framework aims to: 1) automatically learn new generation and transformation process of spatial networks, 2) embed user-specified principles to constrain and regularize the generated spatial networks, and 3) pursue the model interpretability and automatically distill new understandable principles of spatial network process.
Zhao will conduct research activities along the following themes: i) novel spatial and spectral graph decoders for large spatial networks; ii) deep generative modeling and optimization with spatial and topological constraints and regularization; iii) a variety of novel spatial- and spectral- graph transformation strategies; and iv) a novel system for interacting the predefined and distilled principles between human and models.
The techniques developed in this project will focus on benefiting various social and natural science domains by enabling efficient and accurate discovery and synthesis of complex spatial network behavior.
Funding for this award will begin in September 2020 and will end in late August 2025.