Petar Veličković
Also known as Petar Velickovic
Petar Veličković is a research scientist whose graph-learning work includes Graph Attention Networks and Deep Graph Infomax. He studied mathematics in Belgrade and computer science at Cambridge, where he completed a PhD under Pietro Liò before joining Google DeepMind in 2019. His work on neural algorithmic reasoning explores how neural networks can execute algorithmic computation. He is a Senior Staff Research Scientist at Google DeepMind and an Affiliated Lecturer at Cambridge.
- Now at
- Google DeepMind and University of Cambridge
- Works
- 120
- Badges
Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.
Timeline
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2008–2015
From Belgrade to Cambridge
At Matematička gimnazija in Belgrade, he studies specialised mathematics, physics and computer science through 2012. He earns first-class honours in computer science at Trinity College, Cambridge, in 2015.
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2016–2019
A Cambridge PhD
From 2016 to 2019, he works as a research assistant in Cambridge's Artificial Intelligence Group and completes a computer science PhD under Pietro Liò. In 2016, he is a research associate at Nokia Bell Labs; he visits Mila for graph-learning research in 2017 and 2018.
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2017–2018
Graph attention and Deep Graph Infomax
In 2017, he coauthors Graph Attention Networks, which introduces masked self-attention for graph data. In 2018, he coauthors Deep Graph Infomax, an unsupervised method for learning node representations from graph data.
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2019–2021
Research scientist at DeepMind
He joins Google DeepMind in 2019 as a Research Scientist, becomes a Senior Research Scientist in 2020, and becomes a Staff Research Scientist in 2021.
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2021
Neural algorithmic reasoning
In 2021, he coauthors a paper that frames neural algorithmic reasoning as neural networks able to execute algorithmic computation.
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2024
Paper of the Year runner-up
In 2024, he is named runner-up for Current Opinion in Structural Biology's 2023 Paper of the Year for “Everything is Connected: Graph Neural Networks.”
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Since 2021
Teaching at Cambridge
Since 2021, he holds an Affiliated Lectureship in Cambridge's Department of Computer Science and Technology and is an Associate of Clare Hall. He co-teaches the Geometric Deep Learning MPhil course with Pietro Liò.
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As of October 2026
Senior Staff at DeepMind
He is a Senior Staff Research Scientist at Google DeepMind. His research focuses on neural architectures for graph data, algorithmic reasoning and computational biology.
Career
- Since 2019Google DeepMindResearch Scientist, Senior Research Scientist
- Since 2016University of CambridgeResearch Assistant, Affiliated Lecturer
- 2017–2018Mila
- 2016Bell LabsResearch Associate
Education
Sources and credits
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