My research sits at the intersection of machine learning, discrete probability, and theoretical computer science. I am particularly interested in designing algorithms for network analysis that are simple, fast in practice, and that can be rigorously analysed. 

I'm willing to supervise students on a variety of topics related to network analysis and the theoretical foundations of machine learning. These include, for example, the design and analysis of algorithms for unsupervised and supervised learning on graphs.