Petar Veličković

Research Scientist


Besides research and communication, I am an enthusiastic advisor and educator (primarily for machine learning, but I’m occasionally trying to expand into other areas of computer science and mathematics).

The best way to understand something is to explain it to somebody else.

Guided by this quote, I have readily taken up teaching, lecturing and advising throughout my PhD, and am still keen to advise interesting projects, whenever appropriate. Please consult my contact page if you are interested!


Alongside Pietro Liò, I am co-teaching the Geometric Deep Learning (L65) Master’s course at Cambridge. This is a geometry-oriented variation of the Representation Learning on Graphs and Networks (L45) course which we have delivered in 2022 and 2023, which is itself an extended version of the Graph Neural Networks module for the Advanced topics in machine learning or natural language processing (R250) course, which we have delivered in 2021.

Alongside Michael Bronstein, Joan Bruna and Taco Cohen, I have co-taught the Geometric Deep Learning (GDL100) Master’s course for the African Master’s in Machine Intelligence, in 2021 and 2022. This course follows our recently released proto-book and we have made all artifacts and resources publicly available! I have also given an adapted version of this course for the 47th Heidelberg Physics Graduate Days.

Alongside Andrew Dudzik, Bruno Gavranović, João Guilherme Araújo and Pim de Haan, I have co-taught the Categories for AI (Cats4AI) online course, introducing category-theoretic concepts relevant to deep learning research to a diverse group of several thousand attendees. We have made all artifacts and resources publicly available!

I have, on several occasions, also held formal (guest) lectures to undergraduate and postgraduate students:

Furthermore, I have been in charge of designing the practical session on neural networks for the Cambridge MPhil course on Machine Learning and Algorithms for Data Mining (L42).


While at Cambridge, I have been extremely fortunate to (co-)supervise the project work of several students, whose dissertation titles are outlined below. Most of these disseratations have been turned into research publications, and many of the students are now working in prominent AI labs (either as doctoral students or research engineers).

Undergraduate projects

Master’s projects

Reviewer duties

I serve as reviewer and area chair (marked with *) for several major machine learning conferences, journals and workshops, always striving to provide the most actionable feedback possible. My efforts have been recognised with best reviewer awards at all of the “big three” machine learning conferences: NeurIPS 2019, ICML 2020 and ICLR 2021. I have also been honoured to receive the Top Area Chair award at LoG 2022.




Public policy 🇷🇸🇲🇪🇬🇧

I serve on the Scientific Advisory Board of the Institute for AI Research and Development of Serbia.

I have participated in a working group for developing the curriculum for the fourth year of Serbian grammar schools for gifted students in Mathematics and IT. For the first time, we have introduced a regular taught module on Artificial Intelligence and Machine Learning.

Alongside my colleagues from Google DeepMind and researchers from the Raspberry Pi Foundation, I have contributed to and steered the development of Experience AI, a new educational programme that offers cutting-edge KS3 (ages 11–14) resources on artificial intelligence and machine learning for teachers and their students—supporting the next generation of AI leaders in the UK.

I serve on the Advisory Board of the Montenegrin AI Association, a non-governmental organisation founded with an ambition to bring together the Montenegrin AI community.

I’ve participated in developing Serbia’s AI Strategy for 2020-2025, as one of the representatives of Serbian machine learning researchers working abroad. The Government of Serbia had officially adopted it on 26 December 2019.

The Strategy defines objectives and measures for the development of AI, whose implementation should result in economic growth, improvement of public services, advancement of scientific staff, and development of skills for the jobs of the future. Also, the implementation of the Strategy measures should ensure that AI in the Republic of Serbia is developed and implemented in a safe manner and in accordance with internationally recognised ethical principles, in order to exploit the potential of this technology to improve the quality of life of each individual and the society as a whole, as well as to achieve Sustainable Development Goals.

Between 2020 and 2022, I served on the Advisory Board of the Serbian AI Society, an organisation aiming to develop and maintain a plan for the improvement of the Serbian AI ecosystem in the coming years.