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Graph Representation Learning and Beyond (GRL+)
Petar Veličković · Michael M. Bronstein · Andreea Deac · Will Hamilton · Jessica Hamrick · Milad Hashemi · Stefanie Jegelka · Jure Leskovec · Renjie Liao · Federico Monti · Yizhou Sun · Kevin Swersky · Rex (Zhitao) Ying · Marinka Zitnik

Thu Jul 16 11:40 PM -- 10:00 AM (PDT) @ None
Event URL: https://grlplus.github.io/ »

Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of CNNs to graph-structured data, and neural message-passing approaches. These advances in graph neural networks and related techniques have led to new state-of-the-art results in numerous domains: chemical synthesis, 3D-vision, recommender systems, question answering, continuous control, self-driving and social network analysis. Building on the successes of three related workshops from last year (at ICML, ICLR and NeurIPS), the primary goal for this workshop is to facilitate community building, and support expansion of graph representation learning into more interdisciplinary projects with the natural and social sciences. With hundreds of new researchers beginning projects in this area, we hope to bring them together to consolidate this fast-growing area into a healthy and vibrant subfield. Especially, we aim to strongly promote novel and exciting applications of graph representation learning across the sciences, reflected in our choices of invited speakers.

Author Information

Petar Veličković (DeepMind)
Petar Veličković

Petar Veličković is a Staff Research Scientist at DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. He holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. Petar's 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). For his contributions to the area, Petar is recognised as an ELLIS Scholar in the Geometric Deep Learning Program. Within this area, Petar focusses on graph representation learning and its applications in algorithmic reasoning and computational biology. In particular, he is the first author of Graph Attention Networks—a popular convolutional layer for graphs—and Deep Graph Infomax—a popular self-supervised learning pipeline for graphs (featured in ZDNet). Petar's research has been used in substantially improving the travel-time predictions in Google Maps (featured in the CNBC, Endgadget, VentureBeat, CNET, the Verge and ZDNet), and guiding the intuition of mathematicians towards new top-tier theorems and conjectures (featured in Nature, New Scientist, The Independent, Sky News, The Sunday Times and The Conversation).

Michael M. Bronstein (Imperial College London / Twitter)
Andreea Deac (Mila / Université de Montréal)
Will Hamilton (McGill University and Mila)
Jessica Hamrick (DeepMind)
Jessica Hamrick

Jessica Hamrick is a Senior Research Scientist at DeepMind, where she studies how to build machines that can flexibly build and deploy models of the world as well as humans. Her work combines insights from cognitive science with structured relational architectures, model-based deep reinforcement learning, and planning. Jessica received her Ph.D. in Psychology from UC Berkeley, and her M.Eng. in Computer Science and Engineering from MIT.

Milad Hashemi (Google)
Stefanie Jegelka (Massachusetts Institute of Technology)
Jure Leskovec (Stanford University)
Renjie Liao (University of Toronto)
Federico Monti (Twitter)
Yizhou Sun (UCLA)
Kevin Swersky (Google Brain)
Rex (Zhitao) Ying (Stanford University)
Marinka Zitnik (Harvard University)

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