Following on from the success of the 1st and 2nd AthNLP school in 2019 and 2024 respectively, AthNLP 2025 will take place at the campus of NCSR Demokritos in Athens and is organised jointly by NCSR Demokritos, the Athens University of Economics and Business, Athena Research Center, Greece, and Heriot-Watt University, UK.
AthNLP cooperates closely with the organisers of LxMLS, taking place in Lisbon, in July 19-25.
The school will cover a range of NLP topics focusing on machine learning (ML) methods. There will be morning lectures focusing on theoretical aspects, afternoon lab sessions focusing on implementation and experimentation, and evening talks on research topics and perspectives, as well as demos and posters from the participants. The lectures and the evening talks will be given by internationally recognized researchers from academic and industrial research labs. The topics to be covered include: classification, sequence prediction, linear models, neural networks, encoder-decoder architectures, machine translation, large language models, and multimodality.
Our target audience is:
* Researchers and graduate students in the fields of NLP and Computational Linguistics;
* Computer scientists who have interests in natural language processing and machine learning;
* Industry practitioners who desire a more in-depth understanding of these subjects.
While previous experience with the topics will be helpful, the school assumes no previous knowledge of natural language processing and machine learning. The only background assumed is basic mathematics and Python programming.
Features of AthNLP:
* Attendance at the Social Event, daily lunch as well as morning and afternoon coffee breaks are included in the application fee.
* Lecturers are leading researchers in machine learning and natural language processing.
* Students will be able to (optionally) show their current work in poster sessions during coffee breaks.
* In the demo day, students will be able to interact with technical companies and research institutions working in machine learning.
Confirmed Speakers
* Antonis Anastasopoulos, George Mason University, USA, and Archimedes, Athena Research Center, Greece
* Yulan He, King's College London, UK
* Raquel Fernández, University of Amsterdam, The Netherlands
* Ryan McDonald, ASAPP, USA
* Preslav Nakov, MBZUAI, Abu Dhabi, UAE
* Vlad Niculae, University of Amsterdam, The Netherlands
* Anna Rogers, IT University of Copenhagen, Denmark
