Bryan Perozzi is a Research Scientist in Google Research’s Algorithms and Optimization group, where he routinely analyzes some of the world’s largest (and perhaps most interesting) graphs. Bryan’s research focuses on developing techniques for learning expressive representations of relational data with neural networks. In the course of his work, Bryan has advanced the field of graph representation learning with seminal models for graph embedding (e.g. DeepWalk), graph neural networks (e.g. MixHops) and more.

Bryan is an author of 40+ peer-reviewed papers at leading conferences in machine learning and data mining (such as NeurIPS, ICML, ICLR, KDD, and WWW). His doctoral work on learning network representations was awarded the prestigious SIGKDD Dissertation Award.