Eastern European Machine Learning summer school (EEML), Hybrid in Vilnius, Lithuania, 6-14 July 2022, DEADLINE FOR APPLICATIONS April 7, 2022
Razvan Pascanu
Call for Participation (apologies for crossposting)
Eastern European Machine Learning summer school (**with online and in-person sections**)
July 6-14, 2021, Vilnius, Lithuania
Web: https://www.eeml.eu
Email: contact at eeml.eu
Applications are open! Details about the application process https://www.eeml.eu/application.
Application closes: April 7, 2022
Notification of acceptance: Early May 2022.
**Registration will be free for all accepted participants, for both online and in-person attendance.**
Motivation and description
EEML is a machine learning summer school that aims to democratise access to education and research in AI, and improve diversity in the field. The summer school is held yearly in Eastern Europe – this year it will be held in Vilnius, Lithuania. Because of the pandemic, the school will use a hybrid format: first 3 days fully online, last 4 days online and in-person for those who wish to travel to Vilnius; check details on our webpage https://www.eeml.eu/program.
By bringing together (virtually or in-person) high quality lecturers and participants from all over the world, we strive to enable communication and networking among the Eastern European AI communities as well as with researchers from around the world.
The school is open to participants from all over the world. The selection process has equal opportunities and diversity at heart, and will assess interest and knowledge in machine learning. We encourage applications from candidates at all levels of expertise in Machine Learning (beginner, intermediate, advanced). Details about the application process are available online at https://www.eeml.eu/application.
The programme consists of lectures, reading groups, hands-on practical sessions, panel discussions, and more. Some of the core topics to be covered include Reinforcement Learning, Natural Language Processing, Computer Vision, Theory of Deep Learning, Causal Inference.
List of confirmed speakers (so far)
Doina Precup, McGill University & DeepMind
Ferenc Huszar, University of Cambridge
Finale Doshi-Velez, Harvard University
Gintare Karolina Dziugaite, Google Research
Michal Valko, DeepMind
Razvan Pascanu, DeepMind
Suriya Gunasekar, Microsoft Research Redmond
Victor Lempitsky, Skoltech & Samsung
Yee Whye Teh, University of Oxford & DeepMind
Poster session
Participants will have the opportunity to present their research work and interests during virtual poster sessions. The work described does not have to be novel. For example, participants can present their experience of reproducing published work.
Organizers
Doina Precup, McGill University & DeepMind
Razvan Pascanu, DeepMind
Viorica Patraucean, DeepMind
Ferenc Huszar, University of Cambridge
Gintare Karolina Dziugaite, Google Research
Jevgenij Gamper, Vinted
Linas Petkevičius, Vilnius University
Dovydas Čeilutka, Vinted
Linas Baltrūnas, Wayfair
Technical support
Gabriel Marchidan, IasiAI & Feel IT Services
ZoomTV
Partners
Artificial Intelligence Association of Lithuania
Faculty of Mathematics and Informatics, Vilnius University
Local Sponsors
Go Vilnius
More info
contact at eeml.eu
Follow us on Twitter https://twitter.com/EEMLcommunity