Deep learning applications over heterogeneous networks: from multimedia to genes

This research aimed to investigate the synergies between deep learning and heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the state-of-the-art (SOTA) techniques that combine deep learning to heterogeneous graph (het-graph) modeled scenarios. Two main paradigms of collaboration have been identified: 1. Deep learning enhances the scalability and the representation power of graph algorithms and shallow machine learning approaches for graph analysis. 2. Het-graph modeled scenarios help design solution-space exploration biases for deep learning-based optimization algorithms. Moreover, the candidate has chosen two important research fields from industry and academia to identify two open problems where the studied synergisms could be helpful. These open problems were: 1. The online optimization of service function chain deployment in virtualized content delivery networks for live-streaming, 2. The inference of developmental regulatory mechanisms between genes and cis-regulatory elements. Finally, the candidate demonstrated his proficiency in the research field by applying the synergisms identified in the first phase of the research to solve these open problems.