Hello, and welcome to one of my homes on the web! I’m Petar, a Senior Research Scientist at DeepMind. I have a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. My research interests involve devising neural network architectures that operate on nontrivially structured data (such as graphs), and their applications in algorithmic reasoning and computational biology. I have published relevant research in these areas at both machine learning venues (NeurIPS, ICLR, ICML-W) and biomedical venues and journals (Bioinformatics, PLOS One, JCB, PervasiveHealth). In particular, I am the first author of Graph Attention Networks—a popular convolutional layer for graphs—and Deep Graph Infomax—a scalable local/global unsupervised learning pipeline for graphs (featured in ZDNet). Further, my research has been used in substantially improving the travel-time predictions in Google Maps (covered by outlets including the CNBC, Endgadget, VentureBeat, CNET, the Verge and ZDNet).
I contributed to DeepMind's project on Traffic prediction with advanced GNNs now deployed within Google Maps
New paper on pointer-based mechanisms for GNNs:Pointer Graph Networksaccepted as a spotlight talk at NeurIPS 2020!
New paper on GNN expressive power: Principal Neighbourhood Aggregation for Graph Netsaccepted at NeurIPS 2020!