ESSAI-2026: Call for Participation & EurAI Travel Grants
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Gabriella Cortellessa
Mar 6, 2026, 7:01:15 AM (6 days ago) Mar 6
to AIxIA mailing list
Dear AIxIA members,
*Tentative List of Long Courses*
1. AI for Autonomous Robots: Bridging Theory and Practice (Timothy Wiley, The Royal Melbourne Institute of Technology)
2. Specification-Guided Reinforcement Learning (Suguman Bansal, Georgia Institute of Technology)
3. Introduction to Constraint Satisfaction (Roman Barták, Charles University)
I’m sharing the call for participation for the upcoming EurAI Summer School. Please take a look if you are interested or consider sharing it with students and colleagues who might benefit from this opportunity.
Gabriella Cortellessa
AIxIA Secretary
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ESSAI is the annual summer school on AI held under the auspices of the European Association for Artificial Intelligence (EurAI), and in 2026, it will encompass the 2nd International Summer School on Bilateral AI. The fourth edition of ESSAI will be held in Vienna, from 6-10 July 2026.
ESSAI is the largest school of broad AI in Europe, offering courses in all areas of Artificial Intelligence and from a wide range of perspectives. Its thematic scope is analogous to major AI conferences like ECAI, IJCAI and AAAI, covering all AI subdisciplines and their interconnections. ESSAI is a central meeting place for AI students and young researchers to deepen their knowledge, broaden their perspectives, discuss current research, and build connections with other researchers. Vienna awaits you with world-class culture, groundbreaking science, and cozy coffee houses—all in a city celebrated for its quality of life.
*Registration is Open*
Registration fees and dates can be found at: https://essai2026.eu/registration.php
**Early Registration Deadline: April 30, 2026**
Registration fees and dates can be found at: https://essai2026.eu/registration.php
**Early Registration Deadline: April 30, 2026**
*Tentative List of Long Courses*
1. AI for Autonomous Robots: Bridging Theory and Practice (Timothy Wiley, The Royal Melbourne Institute of Technology)
2. Specification-Guided Reinforcement Learning (Suguman Bansal, Georgia Institute of Technology)
3. Introduction to Constraint Satisfaction (Roman Barták, Charles University)
4. Why Is Symbolic Reasoning Computationally Hard? (Andreas Pieris, University of Cyprus, University of Edinburgh)
5. Wikidata: A backbone for Hybrid/Bilateral AI (Axel Polleres & Diego Rincon-Yanez, WU Wien)
6. The Art of Compressing LLMs: Pruning, Distillation, and Quantization Demystified (Liana Mikaelyan & Lavinia Ghita & Harshita Seth & Sergio Perez, NVIDIA)
7. Trustworthy AI (Indrė Žliobaitė, University of Helsinki)
5. Wikidata: A backbone for Hybrid/Bilateral AI (Axel Polleres & Diego Rincon-Yanez, WU Wien)
6. The Art of Compressing LLMs: Pruning, Distillation, and Quantization Demystified (Liana Mikaelyan & Lavinia Ghita & Harshita Seth & Sergio Perez, NVIDIA)
7. Trustworthy AI (Indrė Žliobaitė, University of Helsinki)
8. Data Driven Approaches in (Multi-objective) Bayesian Optimisation (Tinkle Chugh, University of Exeter)
9. Multi-Perspective Reasoning in Knowledge Representation: An Introduction to Standpoint Logic (Timothy Lyon, TU Dresden & Lucía Gómez Álvarez, University Grenoble Alpes)
10. Recommender Systems: Past, Present, and Future (Challenges) (Markus Reiter-Haas & Elisabeth Lex, Graz University of Technology)
11. Logic meets Learning (Vaishak Belle, University of Edinburgh)
12. Beyond Breakpoints: AI for Software Fault Localization (Birgit Hofer & Franz Wotawa, Graz University of Technology)
13. Foundations of Concept-Based Interpretable Deep Learning (Giuseppe Marra, KU Leuven & Pietro Barbiero, IBM Research)
14. Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice (Qing Qu, University of Michigan & Sam Buchanan, UC Berkeley & Yi Ma, University of Hong Kong & Zhihui Zhu, Ohio State University)
15. Decision trees: from efficient prediction to responsible AI (Hendrik Blockeel, KU Leuven)
16. Trustworthy Machine Learning from Data to Models (Bo Han, Hong Kong Baptist University)
17. Reward and Constraint Learning: Foundations for Human-AI Alignment (Sebastian Tschiatschek, University of Vienna)
18. Recurrent GNNs: The Power of Iteration (Jonni Virtema, University of Glasgow & Floris Geerts, University of Antwerp)
19. Knowledge Compilation: Theory, Practice, and Applications (Johannes Fichte, Linköping University & Jean-Marie Lagniez, CRIL, Université d'Artois)
20. AI for Fair and Transparent Decision-Making from Legal and Technical Perspectives (Maria Flórez Rojas & Matias Valdenegro, Groningen University)
21. Uncertainty in Machine Learning: From Aleatoric to Epistemic (Willem Waegeman, Universiteit Gent & Eyke Huellermeier, LMU Munich)
22. Tractable Circuits: A Common Language for Logic, Probability, and Neural Models (Robert Peharz, TU Graz & Adrián Javaloy, University of Edinburgh)
23. Modern Constraint Programming (Emir Demirović, Delft University of Technology)
24. From In-Context Learning to Neuro-Symbolic Reasoning with Large Reasoning Models (Zied Bouraoui & Tanmoy Mukherjee, CRIL CNRS and Univ Artois)
More details on the program can be found at: https://essai2026.eu/courses.php#id-7
9. Multi-Perspective Reasoning in Knowledge Representation: An Introduction to Standpoint Logic (Timothy Lyon, TU Dresden & Lucía Gómez Álvarez, University Grenoble Alpes)
10. Recommender Systems: Past, Present, and Future (Challenges) (Markus Reiter-Haas & Elisabeth Lex, Graz University of Technology)
11. Logic meets Learning (Vaishak Belle, University of Edinburgh)
12. Beyond Breakpoints: AI for Software Fault Localization (Birgit Hofer & Franz Wotawa, Graz University of Technology)
13. Foundations of Concept-Based Interpretable Deep Learning (Giuseppe Marra, KU Leuven & Pietro Barbiero, IBM Research)
14. Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice (Qing Qu, University of Michigan & Sam Buchanan, UC Berkeley & Yi Ma, University of Hong Kong & Zhihui Zhu, Ohio State University)
15. Decision trees: from efficient prediction to responsible AI (Hendrik Blockeel, KU Leuven)
16. Trustworthy Machine Learning from Data to Models (Bo Han, Hong Kong Baptist University)
17. Reward and Constraint Learning: Foundations for Human-AI Alignment (Sebastian Tschiatschek, University of Vienna)
18. Recurrent GNNs: The Power of Iteration (Jonni Virtema, University of Glasgow & Floris Geerts, University of Antwerp)
19. Knowledge Compilation: Theory, Practice, and Applications (Johannes Fichte, Linköping University & Jean-Marie Lagniez, CRIL, Université d'Artois)
20. AI for Fair and Transparent Decision-Making from Legal and Technical Perspectives (Maria Flórez Rojas & Matias Valdenegro, Groningen University)
21. Uncertainty in Machine Learning: From Aleatoric to Epistemic (Willem Waegeman, Universiteit Gent & Eyke Huellermeier, LMU Munich)
22. Tractable Circuits: A Common Language for Logic, Probability, and Neural Models (Robert Peharz, TU Graz & Adrián Javaloy, University of Edinburgh)
23. Modern Constraint Programming (Emir Demirović, Delft University of Technology)
24. From In-Context Learning to Neuro-Symbolic Reasoning with Large Reasoning Models (Zied Bouraoui & Tanmoy Mukherjee, CRIL CNRS and Univ Artois)
More details on the program can be found at: https://essai2026.eu/courses.php#id-7
*EurAI Travel Grants*
The purpose of the EurAI Travel Grant is to support applicants with limited or no access to other funding for travel expenses. The grant can only be used to support your attendance at ESSAI. The amount of the travel grant will be 525€ (amount equal to the early registration fee for students). Applicants must be members of a EurAI member society.
**Deadline for submissions: April 10, 2026**
More details on the selection criteria for Travel Grants: https://eurai.org/applications/travel_grant/ESSAI
The ESSAI 2026 Organising Team
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