Today we’re excited to be joined by return guest Michael Bronstein, Professor at Imperial College London, and Head of Graph Machine Learning at Twitter. We last spoke with Michael at NeurIPS in 2017 about Geometric Deep Learning.  Since then, his research focus has slightly shifted to exploring graph neural networks. In our conversation, we discuss the evolution of the graph machine learning space, contextualizing Michael’s work on geometric deep learning and research on non-euclidian unstructured data. We also talk about his new role at Twitter and some of the research challenges he’s faced, including scalability and working with dynamic graphs. Michael also dives into his work on differential graph modules for graph CNNs, and the various applications of this work. The complete show notes for this episode can be found at twimlai.com/talk/394.