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 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.
Ferenc Huszár is associate professor of Computer Science at Cambridge University, UK, where his current research focuses on fundamental topics of machine learning with a focus on deep learning. Prior to this, he was a research scientist at various technology startups and most recently, at Twitter. He obtained an MSc in Computer Engineering at the Technical University of Budapest, followed by a PHD in Machine Learning from Cambridge University.
Razvan Pascanu is a research scientist at 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 DeepMind in London, working mainly on ML models for perception. 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.
Dovydas Čeilutka is a Director of Data Science and Analytics at Vinted, the President of the Artificial Intelligence Association of Lithuania, and the Lead of Data Science at Turing College. He has experience in various aspects of machine learning and data science - from building machine learning models for production usage to leading machine learning teams to teaching people data science. He is passionate about the productivity of data scientists, the management of machine learning projects, and the interaction between business and AI.
Jev Gamper is a Staff Decision Scientist at Vinted, leading experimentation and causal inference efforts across the organisation. Jev did his Msc in Applied Mathematics at Warwick Univeristy, and is a PhD Candidate at Warwick University. His research invovled applications of statistical and machine learning methods to medical imaging, astronomy, remote sensing, and climate modeling.
Karolina Dziugaite is a Senior Research Scientist at Google Research, Brain Team, an adjunct professor in the McGill University School of Computer Science, and an associate industry member of Mila, the Quebec AI Institute. She was a member of the Institute of Advanced Studies in 2020 and a Simons Fellow at the Simons Institute for the Theory of Computing at UC Berkeley in 2019. She obtained her PhD at the University of Cambridge. Her research combines theoretical and empirical approaches to understanding deep learning, generalization, and sparsity.
Linas Baltrunas is a director of search and recommendations at Wayfair. He obtained his Ph.D. at Free University of Bolzano, working with Francesco Ricci on context-aware collaborative filtering and other personalization models. Linas is active in the personalization community and is currently working on various systems and applications of machine learning in the industry.
Linas Petkevičius is Research Scientist at the faculty of Mathematics and Informatics at Vilnius University, where his current research interests focus on interdisciplinary topics solving via deep learning, board member of Artificial intelligence association of Lithuania, and also working in R&D at Neurotechnology. Linas obtained his bachelor and master's degree in statistics, followed by a Ph.D. in Informatics at Vilnius University.
Every year, we organise the summer school in different locations from Eastern Europe.
The 2021 edition is organised online, in collaboration with Budapest.
The 2019 edition was organised 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.
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.