Abstract

We are delighted to invite you to our first talk of Michaelmas, organised alongside the ML in PL Association. We are hosting Mateusz Malinowski and Petar Veličković, both senior researchers at Google DeepMind, in the Cambridge Union’s Debating Chamber.

The event will begin at 1pm on Saturday 30th October. If you are interested please fill out the registration form (https://cambridge2021.paperform.co/), and see the Facebook event (https://www.facebook.com/events/3071717486404137).

AGENDA

1pm – Opening remarks and talk from Petar Veličković, 2pm – Talk from Mateusz Malinowski, 2:45pm – Break, 3pm – Discussion panel, 4pm – Closing remarks.

SPEAKER BIOS

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.

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).