Deep Learning Based Approaches to Synthetic Data Generation

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this project, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) that is novel and scalable to address this research problem. We aim to make both theoretical advances by developing new algorithms, as well as release easy-to-use implements for others addressing similar challenges.

Faculty Supervisor:

Periklis Andritsos

Student:

Partner:

Arima Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: