Learning Approaches to Graph Structure Discovery

The project would explore the use of modern techniques from the field of Machine Learning to identify networks from observational data. This is an important area of research in fields such as neuroscience and genetics, where it can shed light on the nature of various disease. Other applications include discovering influencers and communities in social networks. These problems are normally untenable in the general case and techniques rely on deriving mathematical approximations, which often make many assumptions, and require lengthy time to formulate by expert researchers. In this project we explore methods of automatically deriving approximations to these problems based simulated examples, which are much easier to formulate than mathematical approximations. We hope the outcome of the project can provide
useful tools for researchers studying neuroimaging, genomics, and social networks to solve problems in their field.

Faculty Supervisor:

Raquel Urtasun

Student:

Partner:

Inria Saclay - Île-de-France Research Centre

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Globalink Research Award