ESSAI 2025

🌟 At ESSAI 2025 you could join Maria Deprez too and dive into AI for Medical Imaging.

🌟 You discovered practical solutions to medical imaging challenges, learned to work with limited labeled data using smart tools like one- or few-shot learning and synthetic data training.

🚀 Also, there was a place for you to tackle domain shifts with data augmentation and domain adaptation.

🧠 You could explore implicit neural representations for more accurate and flexible image reconstruction.

🌞 We hope you didn't miss this opportunity and leveled up skills this summer!

🌟 Dive into the Future! 🌟
Join us at ESSAI 2025 with Olivier Ezratty at the EPITA Research Lab's Quantum Energy Initiative. Explore how AI and quantum computing can supercharge each other in this eye-opening summer school session!
🔍 Discover:
- Quantum computing basics and cutting-edge industry insights.
- The synergy between AI and quantum in error correction, circuit synthesis, and more.
- Revolutionary quantum-assisted machine learning for data processing miracles.
Get ready to be part of the quantum revolution! 🎓✨

🎓 Ready to dive into the future of tech? Join us for "From Pixels to Words: Frontiers of Multi-Modal Vision-Language Learning" at ESSAI 2025 with Jana Košecká from George Mason University.
🌟 Discover how today's foundational models are revolutionizing computer vision—think classification, detection, segmentation, and even image generation! We'll explore the magic behind self-supervised learning, contrastive learning, and vision transformers, all powered by massive datasets.
🚀 But wait, there's more! We'll delve into the cutting-edge world of multi-modal vision-language models. Imagine bridging visuals and text with models like Segment Anything, CLIP, and Flamingo, unlocking capabilities like image captioning and visual question answering. 🖼️📝
🔍✨ Get ready for an exciting journey through the current state of vision models and the game-changing integration of vision and language. Plus, we’ll discuss future challenges and directions in perception, language, and reasoning. Don't miss out on this insightful session!

🔥 Can AI crack the cancer cure code? Join the journey! 🔍

📚 Discover next-gen sequencing and how AI decodes the genome puzzle. Learn from Michal Kováč on using AI for spotting DNA mutations—potential cancer solutions.

💡 Get hands-on with machine learning, deep learning, and language models revolutionizing drug development.

👨‍💻 Michal Kováč, an Associate Professor at the Slovak University of Technology and the mind behind research group BrAInWorks, will guide you. His path includes the University of Basel, Oxford, and Roche.

🔬 Don’t miss exploring tech and health for groundbreaking cancer therapy discoveries.

🧠 Ready to join the AI revolution in medicine?

☑️ 5. LEARNING PATH - AI & Reasoning Models

➡️ Dive into the fascinating world of symbolic AI, logic programming, and computational reasoning! This path offers a comprehensive exploration of both foundational and advanced approaches to formal reasoning in AI. Are you ready to delve into topics like automated planning, constraint satisfaction, Datalog, and answer set programming? You'll learn how to integrate these techniques with large language models, shaping AI systems that can reason about actions, knowledge, and goals in both discrete and continuous environments.

Courses Include:

🔎 Automated Planning in the Continuous World
Instructor: Mikhail Soutchanski
Discover how planning algorithms adapt to the continuous world and enhance AI’s decision-making capabilities. Ready to map out your path?

🔎 Deep Reasoning in AI with Answer Set Programming and LLMs
Instructors: Francesco Ricca; Mario Alviano*
Explore deep reasoning techniques and see how they mesh with large language models to create intelligent solutions. What's your take on the future of AI reasoning?

🔎 Foundations and Explainability of Datalog
Instructors: Andreas Pieris; Marco Calautti
Unearth the core principles of Datalog and its role in creating transparent and explainable AI systems. Why is explainability a top priority for you?

🔎 Introduction to Constraint Satisfaction
Instructor: Roman Barták
Master the art of designing systems that thrive on constraints. Curious about how these constraints can power smarter, more efficient solutions?

👍Start building robust AI systems today! Let's embark on this journey together.

🌟 4. LEARNING PATH - AI for Autonomous Systems & Robotics
➡️ Explore the Future of AI with Our Learning Path in Autonomous Systems & Robotics!
Dive deep into the fascinating world where AI meets autonomy. This path brings together essential concepts like reinforcement learning, human-robot interaction, cognitive architectures, and strategic reasoning in multi-agent environments. It's all about turning theoretical models into real-world magic!

Courses to Transform Your Understanding:
🔎 Self-Governing Systems
Instructor: Jeremy Pitt

🔎 AI for Autonomous Robots: Bridging Theory and Practice
Instructor: Timothy Wiley

🔎 Back to the Future of Cognition and Control in Robotics: Robustness, Rationality, and Explainable Agency
Instructor: Mohan Sridharan

🔎 Strategic AI: Bridging Game Theory and Multi-Agent Systems via Autoformalization
Instructors: Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi

🚀 Ready to bridge the gap between theory and practice? Which course are you most excited about? Let us know in the comments!

🧠 3. LEARNING PATH - Machine Learning and Robust AI
➡️ Are you ready to explore the frontier of machine learning models that stand strong against adversarial challenges? This learning path is designed to equip you with the knowledge and skills to create resilient, secure AI systems. Dive into intriguing topics like adversarial robustness, machine unlearning, and more!

Courses Offered:

🔍 AI for Security: Exploring Adversarial Learning and the Transferability of Adversarial Attacks
Instructor: Michail Alexiou
Discover how AI and security intersect. What strategies can we use to defend against adversarial attacks?

🔍 Bridging Adversarial Learning and Data-Centric AI for Robust AI
Instructors: Vincenzo Pasquadibisceglie; Giuseppina Andresini
Explore how merging these approaches enhances AI robustness. What can data tell us about strengthening AI systems?

🔍 Machine Unlearning: Theory, Methods, and Evaluations with Hands-On Insights
Instructors: Giovanni Stilo; Flavio Giobergia
Learn how to effectively "unlearn" in AI. How does this approach impact model integrity and ethics?

🔍 Higher-Order Network Analysis: Topology, Machine Learning, and Applications
Instructors: Maria Sofia Bucarelli; Federica Baccini
Delve into the fascinating world of network analysis. How can topology transform machine learning applications?

🔍 Query Languages and Graph Neural Networks
Instructors: David Tena Cucala; Michael Benedikt
Uncover the power of graph neural networks and their applications. How do query languages enhance our understanding?

💬✨ Ready to take the leap into a future of secure, adaptable AI? Comment below or share your thoughts on which course excites you the most! Let's build the future of AI together.

🌐 2. LEARNING PATH - AI for Decision Making & Optimization
➡️ Are you ready to dive into the world where AI meets smart decision-making? 🤖✨ Our next learning path on AI for Decision Making & Optimization is designed just for you!

📈 Explore powerful AI techniques that focus on making informed decisions and optimizing under uncertainty. You'll delve into Bayesian optimization, master recommender systems, and tackle the challenges of uncertainty in machine learning—all with real-world applications in planning and predictive modeling.

☑️ Courses include:

🔎 Data Driven Approaches in (Multi-objective) Bayesian Optimization
Instructor: Ti**le Chugh
Discover how data-driven methods can revolutionize optimization and explore multi-objective strategies. 📊

🔎 Neural Recommender Systems: Theory, Methods, and Applications
Instructors: Federico Siciliano & Giulia Di Teodoro
Learn the ins and outs of recommender systems and their role in personalizing user experiences. 🎯

🔎 Uncertainty in Machine Learning - Towards Trustworthy AI Models
Instructors: Marco Zullich & Matias A. Valdenegro Toro
Build AI models you can trust, focusing on managing uncertainty effectively. 🔒

📚 Are you excited to enhance your skills and build models that support efficient and robust decisions with complex, high-dimensional data?
🙌 We are looking forward to seeing you on ESSAI 2025!

1. LEARNING PATH - Trustworthy & Explainable AI

🤖 Are you ready to dive into the world of AI transparency and accountability?
➡️ At ESSAI 2025, our learning paths are crafted to guide you in choosing the ideal courses that align with your goals.

🌍This path delves into the ethical, legal, and technical dimensions of AI systems to ensure they are transparent, interpretable, and aligned with societal values. Explore the intersection of formal methods, legal frameworks, human rights, and argumentation-based explanations that make AI both effective and trustworthy.

Courses:
🔎 Formal Verification of Symbolic and Connectionist AI: A Path to Higher Quality Software
Instructors: Julien Girard-Satabin, Dorin Doncenco, Zakaria Chihani
Dive into methods that enhance software quality. Ready to elevate your skills? 🚀

🔎 Human Rights to AI System Specifications
Instructors: Asimina Mertzani, Nicholas Vadivoulis
Learn how human rights inform AI system design. How can this course influence your AI practices?

🔎Ethics and Law in Trustworthy AI: Foundations and Applications
Instructors: Juraj Podroužek, Matúš Mesarčík
Discover how ethical and legal considerations shape the AI landscape. How will you apply these principles in your future projects?

🔎 Explainable AI via Argumentation: Theory & Practice
Instructors: Nikolaos Spanoudakis, Antonis Kakas
Master the art of making AI systems understandable. Want to see your AI work make more sense?

💡 Join us and ensure your AI systems are as accountable as they are innovative.
Which course sparks your curiosity the most? Engage with us and let’s explore the future of AI together! ✨

📣 𝐄𝐒𝐒𝐀𝐈 𝟐𝟎𝟐𝟓 – European Summer School on AI: Last Chance for Early Registration!

Dear colleagues and AI enthusiasts,
We’re entering the final hours to secure Early Bird tickets for ESSAI 2025 – the European Summer School on Artificial Intelligence!
🕛 Deadline: May 26 at 24:00
🎟 Early Registration Fees: €350 / €450 / €600
⏩ After that, prices increase to €450 / €550 / €750
🌐 Register now: www.essai2025.eu
📍 Date & Location: June 30 – July 4, 2025 | Bratislava, Slovakia
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In just a few years, ESSAI has evolved into Europe’s flagship summer school for AI, hosted in leading academic cities. After successful editions in Ljubljana (2023) and Athens (2024), Bratislava is proud to welcome the AI community in 2025!
ESSAI is more than just a school—it’s a vibrant meeting point for cutting-edge research, deep learning, and meaningful connections across academia and industry.
________________________________________
👉 To help you navigate the 20 available courses, you can choose to follow one of our curated learning paths.
🔷 Trustworthy & Explainable AI
Explore how AI can be designed to meet legal, ethical, and societal expectations. Learn to build transparent and accountable systems.
🔷 AI for Decision Making & Optimization
Master data-driven decision-making and optimization techniques for complex, uncertain environments.
🔷 Machine Learning & Robust AI
Develop reliable and secure models. Topics include adversarial learning, data-centric AI, and unlearning.
🔷 AI for Autonomous Systems & Robotics
Discover how AI powers autonomy—from reinforcement learning to cognitive control and human-robot collaboration.
🔷 AI & Reasoning Models
Dive into formal reasoning and symbolic AI—from logic programming and planning to explainability and LLM integration.
🌙 Evening ACAI Tutorials
Open-access sessions on frontier AI topics: quantum computing, large language modeling, vision-language modeling, distributed AI, and more. Ideal for broadening your horizons.
________________________________________
🌍 Who should attend?
PhD students, postdocs, researchers, and AI-driven professionals—ESSAI 2025 offers a unique opportunity to engage with top experts, tackle advanced topics, and strengthen Europe’s AI research ecosystem.
We look forward to welcoming you to Bratislava—the AI capital of summer 2025!

Home European Summer School on Artificial Intelligence (ESSAI) intends to serve as a central hub for PhD students and young researchers working in all aspects of AI. The ESSAI Summer School covers one week of courses in the summer, at different sites around Europe, for both beginning and advanced student...

🧠 MAKING ESSAI AM(Ai)ZING EVENT WITH 20 COURSES (and you can visit all of them) 🌐

➡️ Yes, you read it right. The ESSAI 2025 will be AI summer school with many courses and what is more interesting, with renowned lecturers from all around the world!

🌇 Visit the capital of Slovakia and get to know AI from multiple perspectives from June 30th - July 4th.

☑️ Here we can tease you with names of the courses:
1. AI for Autonomous Robots: Bridging Theory and Practice
2. Automated Planning in the Continuous World
3. Back to the Future of Cognition and Control in Robotics: Robustness, Rationality, and Explainable Agency
4. Bridging Adversarial Learning and Data-Centric AI for Robust AI
5. Data Driven Approaches in (Multi-objective) Bayesian Optimisation
6. Explainable AI via Argumentation: Theory & Practice
7. Deep Reasoning in AI with Answer Set Programming and LLMs
8. Distributional reinforcement learning
9. Ethics and Law in Trustworthy AI: Foundations and Applications
10. Machine Unlearning: Theory, Methods, and Evaluations with Hands-On Insights
11. Formal verification of symbolic and connectionist AI: a way toward higher quality software
12. Query Languages and Graph Neural Networks
13. Foundations and Explainability of Datalog
14. Higher-Order Network Analysis: Topology, Machine Learning, and Applications
15. Human Rights to AI System Specifications
16. Neural Recommender Systems: Theory, Methods, and Applications
17. Introduction to Constraint Satisfaction
18. Strategic AI: Bridging Game Theory and Multi-Agent Systems via Autoformalization
19. Uncertainty in Machine Learning - Towards Trustworthy AI Models
20. AI for Security: Exploring Adversarial Learning and the Transferability of Adversarial Attacks

ℹ️ Do you want to know more about these cool courses?
🔗 You will find them here: essai2025.eu/courses/

🙌 We look forward to seeing you!