Petar Veličković The Scalable Parallel Computing Lab's *SPCL_Bcast* seminar continues with *Petar Veličković**of **DeepMind, and University of Cambridge* presenting on *Capturing Computation with Algorithmic Alignment*. Everyone is welcome to attend (over Zoom)! *When:* Thursday, 21st March, 6PM CET *Where:* Zoom Join <https://spcl.inf.ethz.ch/Bcast/join> *Abstract:* What makes a neural network better, or worse, at fitting certain tasks? This question is arguably at the heart of neural network architecture design, and it is remarkably hard to answer rigorously. Over the past few years, there have been a plethora of attempts, using various facets of advanced mathematics, to answer this question under various assumptions. One of the most successful directions -- algorithmic alignment -- assumes that the target function, and a mechanism for computing it, are completely well-defined and known (i.e. the target is to learn to execute an algorithm). In this setting, fitting a task is equated to capturing the computations of an algorithm, inviting analyses from diverse branches of mathematics and computer science. I will present some of my personal favourite works in algorithmic alignment, along with their implications for building intelligent systems of the future. *Biography:* Petar is a Staff Research Scientist at Google DeepMind, an 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. 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). For his contributions, he is recognized as an ELLIS Scholar in the Geometric Deep Learning Program. Particularly, he focuses on graph representation learning and its applications in algorithmic reasoning (featured in VentureBeat). 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). His research has been used in substantially improving travel-time predictions in Google Maps (featured in CNBC, Endgadget, VentureBeat, CNET, the Verge, and ZDNet), and guiding the intuition of mathematicians towards new top-tier theorems and conjectures (featured in Nature, Science, Quanta Magazine, New Scientist, The Independent, Sky News, The Sunday Times, la Repubblica, and The Conversation). More details & future talks <https://spcl.inf.ethz.ch/Bcast/> Scalable Parallel Computing Lab (SPCL) Department of Computer Science, ETH Zurich Website <https://spcl.inf.ethz.ch> X(Twitter) <https://twitter.com/spcl_eth> YouTube <https://www.youtube.com/@spcl> GitHub <https://github.com/spcl>