We were delighted to host our first talk of Michaelmas term alongside MLinPL, welcoming Petar Veličković and Mateusz Malinowski. The event was held in the Cambridge Union’s Debating Chamber.

Both speakers are distinguished scientists with a history of high-profile papers, and senior researchers at DeepMind.

Petar Veličković’s talk was titled “Everything is connected: deep learning on graphs”, and was an entry-level bird’s eye view on GNNs and their applications. Mateusz Malinowski’s talk was titled “Holistic Vision: Reasoning, Interactions, Multimodality”, discussing the role of reasoning in modern computer vision.

Petar Veličković speaking to us at the Cambridge Union.


Petar Veličković is a Staff Research Scientist at DeepMind, and an Affiliated Lecturer at the University of Cambridge. He holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. His research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data (a topic he’s co-written a proto-book about). Within this area, Petar focuses on graph representation learning and its applications in algorithmic reasoning and computational biology. He has 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, he is 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, his 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).

Mateusz Malinowski is a Research Scientist at DeepMind. His work concerns computer vision, natural language understanding, reasoning and scalable training. His main contribution is creating foundations and various methods that answer questions about images and proposing a scalable alternative to backprop training mechanism. Mateusz has received a PhD from Max Planck Institute for Informatics and received multiple awards for his contributions to computer vision.