Call for Participation: 4th European Summer School on Artificial Intelligence (ESSAI 2026)
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Mantas Simkus
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Feb 25, 2026, 11:00:05 AM (4 days ago) Feb 25
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Call for Participation
4th European Summer School on Artificial Intelligence (ESSAI 2026) 2nd International Summer School on Bilateral AI 6-10 July 2026, Vienna, Austria https://essai2026.eu
*General Information*
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. 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.
***Early Registration Deadline: April 30, 2026*** ***Register now to secure your spot at ESSAI 2026!***
*Location*
The fourth edition of ESSAI will be held in Vienna, a city where history and innovation walk side by side. Home to world-renowned universities and research centers, Vienna has inspired great thinkers for centuries—think of Erwin Schrödinger, Lise Meitner, Sigmund Freud, Kurt Gödel, Ludwig Wittgenstein, or the Vienna Circle. Today, Vienna continues to welcome students, scholars, and curious minds from around the globe. Vienna awaits you with world-class culture, groundbreaking science, and cozy coffee houses—all in a city celebrated for its quality of life.
*Tentative List of Long Courses* 1. AI for Autonomous Robots: Bridging Theory and Practice 2. Specification-Guided Reinforcement Learning 3. Introduction to Constraint Satisfaction 4. Why Is Symbolic Reasoning Computationally Hard? 5. Wikidata: A backbone for Hybrid/Bilateral AI 6. The Art of Compressing LLMs: Pruning, Distillation, and Quantization Demystified 7. Trustworthy AI 8. Data Driven Approaches in (Multi-objective) Bayesian Optimisation 9. Multi-Perspective Reasoning in Knowledge Representation: An Introduction to Standpoint Logic 10. Recommender Systems: Past, Present, and Future (Challenges) 11. Logic meets Learning 12. Beyond Breakpoints: AI for Software Fault Localization 13. Foundations of Concept-Based Interpretable Deep Learning 14. Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice 15. Decision trees: from efficient prediction to responsible AI 16. Trustworthy Machine Learning from Data to Models 17. Reward and Constraint Learning: Foundations for Human-AI Alignment 18. Recurrent GNNs: The Power of Iteration 19. Knowledge Compilation: Theory, Practice, and Applications 20. AI for Fair and Transparent Decision-Making from Legal and Technical Perspectives 21. Uncertainty in Machine Learning: From Aleatoric to Epistemic 22. Tractable Circuits: A Common Language for Logic, Probability, and Neural Models 23. Modern Constraint Programming 24. From In-Context Learning to Neuro-Symbolic Reasoning with Large Reasoning Models