Submitted by 2021020 on Mon, 06/13/2022 - 14:00
Dr. Mengmeng Liu
Assistant Professor
Computer Science

Dr. Mengmeng Liu is an assistant professor at GTSI and an adjunct instructor in the School of Computing Instruction at Georgia Tech. Before joining GTSI, she worked as a big data engineer in FINRA for 2 years. She has multidisciplinary professional background in geography, remote sensing, transportation, urban planning, statistical analysis, and computational science. She has over ten years of experience in processing and analyzing multi-source spatial data, two years of experience with big data analysis. She designed and developed a data-driven spatial sustainability assessment framework, which can systematically evaluate the sustainability of a project and provided support information for decision-making.

She received her Ph.D. in Computational Science and Engineering in 2019 under the supervision of Prof. J. David Frost at Georgia Institute of Technology, Atlanta. She has earned two master’s degrees in Computational Science and Engineering from Georgia Tech, in Cartography and GIS from the Institute of Remote Sensing and Digital Earth, CAS. Her research focuses on Data-driven Spatial Sustainability Assessment, Spatio-temporal Data Analysis, Mining and Visualization, and Deep Learning. She was awarded the Ray C. Anderson Fellowship in Sustainable Systems. She has published over ten journal and conference papers.

  • PhD: Computational Science & Engineering, Georgia Institute of Technology, 2019/08                            
  • MS: Computational Science & Engineering, Georgia Institute of Technology, 2017/12

              Cartography and GIS, Institute of Remote Sensing and Digital Earth, CAS, 2013/06

  • BS: Cartography and GIS, Henan University, 2010/06
Research Interests
  • Data-driven Spatial Sustainability Assessment: sustainability assessment framework, smart cities;
  • Spatiotemporal Data Analysis, Mining and Visualization: spatiotemporal analysis of sustainability status, big data and sustainable development, visualization of evaluation results;
  • Deep Learning: deep learning facilitates sustainability evaluation, such as automatic creation of evaluation indicators, self-learning of sustainability evaluation cases.