[visionlist] Eastern European Machine Learning summer school (EEML), Hybrid in Vilnius, Lithuania, 6-14 July 2022, DEADLINE FOR APPLICATIONS April 7, 2022

Viorica Patraucean vpatrauc at gmail.com
Wed Mar 23 06:36:11 -04 2022

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
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

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)

Catalina Cangea, DeepMind
Doina Precup, McGill University & DeepMind
Ferenc Huszar, University of Cambridge
Finale Doshi-Velez, Harvard University
Gintare Karolina Dziugaite, Google Research
Michal Valko, DeepMind
Moritz Hardt, Max Planck
Razvan Pascanu, DeepMind
Suriya Gunasekar, Microsoft Research Redmond
Thomas Kipf, Google Research
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.


Doina Precup, McGill University & DeepMind
Razvan Pascanu, DeepMind
Viorica Patraucean, DeepMind
Ferenc Huszar, University of Cambridge
Gintare Karolina Dziugaite, Google Research
Dovydas Čeilutka, Vinted
Jevgenij Gamper, Vinted
Linas Baltrūnas, Wayfair
Linas Petkevičius, Vilnius University

Technical support

Gabriel Marchidan, IasiAI & Feel IT Services


Artificial Intelligence Association of Lithuania
Faculty of Mathematics and Informatics, Vilnius University

Sponsors confirmed so far

Go Vilnius
Visage Technologies

More info

contact at eeml.eu
Follow us on Twitter https://twitter.com/EEMLcommunity
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