Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Learning from relational data is crucial for modeling the processes found in many application domains ranging from computational biology to social networks. In this project, we propose to work on developing modeling techniques that combine the advantages of the approaches found in two fields of study: Machine Learning (through graph neural networks) and Statistical Learning (through statistical relational learning methods). By combining the advantages of both approaches, we aim to obtain better prediction results for an array of problems such as classification and link prediction in relational data.
Louigi Addario-Berry
Benoît Corsini
Element AI
Statistics / Actuarial sciences
Professional, scientific and technical services
McGill University
Accelerate
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find ProjectsThe strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.