In this project, we aim to develop GCN-based GAN models to predict spatio-temporal evolution using open human mobility datasets – SafeGraph (https://www.safegraph.com/).

M.S. are welcome to register my research credits during the semesters and summer. The student involved in this project will develop GAN based models that can predict. spatio-temporal evolution of human mobility. Students with coding skills and past experience in analyzing spatio-temporal data and training GAN and GCN/GNN models are preferred. Skill requirements are:

  1. Familiar with Python. Generating figures, graphs, tables, or statistical models to present results with python.
  2. Familiar with GAN and its variant models.
  3. Familiar with GCN/GNN.
  4. Having past experience in doing research and demonstrating independent research capabilities.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Sharon Di
  • Center/Lab: Civil Engineering
  • Location: Morningside Mudd
  • selfdriving, AI

Project Timeline

  • Earliest starting date: 1/24/2023
  • End date: 12/24/2023
  • Number of hours per week of research expected during Spring-Summer 2023: ~15

Candidate requirements

  • Skill sets: big data processing, programming, GAN training
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes