About
What is EEML?
Eastern European Machine Learning (EEML) summer school is a one-week summer school around core topics regarding machine learning and artificial intelligence. The summer school includes both lectures and practical sessions (labs) to improve the theoretical and practical understanding of these topics. The school is organised in English and is aimed in particular at graduate students, although it is open to anyone interested in the topic.
Who we are?
Doina Precup is associate dean of research at the faculty of science at McGill University, Canada research chair in machine learning, and a senior fellow at the Canadian Institute for Advanced Research. She also heads the Montreal office of Google DeepMind. Doina obtained her bachelor of science degree in computer science and engineering at Technical University of Cluj-Napoca, followed by master's degree and a Ph.D. at University of Massachusetts Amherst.
Razvan Pascanu is a Research Scientist at Google DeepMind in London. He obtained his bachelor and master degree in Germany, at Jacobs University, and the Ph.D. at Université de Montreal, working with Yoshua Bengio on optimization, recurrent models, and deep learning in general. His interests range from topics like optimization, neural networks to deep reinforcement learning, continual learning and structured neural networks models.
Viorica Patraucean is a Research Scientist at Google DeepMind in London, working mainly on ML models for perception and their evaluation. She did her undergrad in computer science and engineering at Military Technical Academy in Bucharest, followed by Master's and Ph.D. at Institut National Polytechnique de Toulouse. She then worked as research associate at Ecole Polytechnique Paris and University of Cambridge, focusing on analysis of 3D shapes and videos.
Petar Veličković is a Research Scientist at Google DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. He holds a Ph.D. in Computer Science from the University of Cambridge (Trinity College). Petar's research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data. Particularly, Petar focuses on graph representation learning and its applications in algorithmic reasoning. Petar's research has been used in substantially improving travel-time predictions in Google Maps, guiding intuition of mathematicians towards new top-tier theorems and conjectures, and powering state-of-the-art football analytics (in partnership with Liverpool FC).
Nemanja Rakićević is a Research Scientist at Google DeepMind, working on Exploration in Reinforcement Learning and Agent-Task co-evolution approaches. He finished his Ph.D. studies in the Robot Intelligence Lab of Imperial College London, with the thesis titled "Parameter space abstractions for diversity-based policy search". During his Ph.D., Nemanja interned in DeepMind at the Robotics Lab, hosted by Francesco Nori.
Dubravko Ćulibrk is a Full Professor of Information Systems Engineering at the Department of Industrial Engineering and Management of the Faculty of Technical Sciences, University of Novi Sad, Serbia, committed to developing and heading the Artificial Intelligence Research and Development Institute of Serbia. He completed his Ph.D. degree at the Florida Atlantic University, USA, where he was affiliated with the Center for Coastline Security and the Center for Cryptology and Information Security. Dubravko's current research interests include: Neural Networks and Deep Learning, Computer Vision, Machine Learning and Data Science, Multimedia, Visual Attention and Image/Video Processing.
Every year, we organise the summer school in different locations from Eastern Europe.
The 2023 edition was organised in person, in Kosice Slovakia, in collaboration with ESET, Technical University of Kosice, and AI Slovakia.
The 2022 edition was organised in a hybrid format, with the in-person component in Vilnius Lithuania, in collaboration with AI Association of Lithuania, Vilnius University, VilniusTech, and Go Vilnius.
The 2021 edition was organised online, in collaboration with Wigner Research Center for Physics Budapest.
The 2020 edition was organised online, in collaboration with GMUM and MLinPL.
The 2019 edition was organised in person, in collaboration with Marius Leordeanu and Traian Rebedea from Politehnica University of Bucharest, Elena Burceanu from Bitdefender, and Gabriel Marchidan from IasiAI/Feel IT Services.
The 2018 edition was organised in collaboration with Luigi Malago and Razvan Florian from Romanian Institute of Science and Technology.
Why are we organising EEML?
Machine learning and artificial intelligence have become major topics in both academia and industry, with a tremendous potential to impact our everyday life. While a lot of progress happened in the last decade, this progress has been localized in a few places mostly in USA, Canada and to some extent Western Europe, China, Japan.
Our main aim is to popularise topics around machine learning and artificial intelligence more broadly in Europe and specifically in Eastern Europe, and to encourage research in these fields.
We firmly believe in equality and diversity and we strive to improve the access to education to everyone interested in machine learning and artificial intelligence.
We strive to have a diverse list of lecturers and students, with researchers operating in Eastern Europe and in well established research centers around the world. We hope this school will create an additional communication channel bridging the gaps between different communities. More information about our motivation can be found in the report summarising the first edition.
Code of conduct
The school follows the code of conduct detailed here. Everyone involved in this school is required to follow it: organisers, speakers, participants, sponsors, volunteers.
Contact information
Please write to us at contact@eeml.eu. Follow us on X.