Lectures

Lecture 1/3 “Introduction to Multi-Agent Reinforcement Learning”

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Lecture 2/3 “If Multi-Agent Foundation Models is the Answer, What is the Question?”

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Lecture 3/3 “Becoming an AI Researcher: Practical Advice for Graduate Students”

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Lecture 1/3 “Predictive coding: training neural networks with local rules”

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Lecture 2/3 “Predictive coding: advantages over backpropagation”

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Lecture 3/3 “Predictive coding: implementation”

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Lecture 1/3 “Foundations of Guardrailing Agentic AI via Temporal Synthesis”

ABSTRACT: This course presents temporal synthesis as a foundation for guardrailing agentic AI. Temporal synthesis studies the automatic synthesis of interactive programs (strategies) from declarative specifications expressed in temporal logic. In this course, we show how temporal synthesis provides a practical foundation for strategic reasoning in autonomous AI systems. The key to this research path lies in the rich body of concepts developed in reasoning about actions and planning, combined with a precise treatment of nondeterministic environments and temporal objectives. In such settings, plans must be treated as strategies rather than being blurred with individual execution traces, and goal satisfaction evolves during execution rather than being reducible to reaching states with fixed properties, as in classical planning. These features are naturally captured within the temporal synthesis framework. Technically, we study the structure of the entire space of strategies satisfying a specification. Rather than assuming strategies to be observable, we focus on characterizing the set of execution traces that are compliant with a given strategy space. This perspective provides formal support for guardrailing and responsibility attribution in systems composed of multiple, independently acting autonomous agents.

Lecture 2/3 “Foundations of Guardrailing Agentic AI via Temporal Synthesis”

ABSTRACT: This course presents temporal synthesis as a foundation for guardrailing agentic AI. Temporal synthesis studies the automatic synthesis of interactive programs (strategies) from declarative specifications expressed in temporal logic. In this course, we show how temporal synthesis provides a practical foundation for strategic reasoning in autonomous AI systems. The key to this research path lies in the rich body of concepts developed in reasoning about actions and planning, combined with a precise treatment of nondeterministic environments and temporal objectives. In such settings, plans must be treated as strategies rather than being blurred with individual execution traces, and goal satisfaction evolves during execution rather than being reducible to reaching states with fixed properties, as in classical planning. These features are naturally captured within the temporal synthesis framework. Technically, we study the structure of the entire space of strategies satisfying a specification. Rather than assuming strategies to be observable, we focus on characterizing the set of execution traces that are compliant with a given strategy space. This perspective provides formal support for guardrailing and responsibility attribution in systems composed of multiple, independently acting autonomous agents.

Lecture 3/3 “Foundations of Guardrailing Agentic AI via Temporal Synthesis”

ABSTRACT: This course presents temporal synthesis as a foundation for guardrailing agentic AI. Temporal synthesis studies the automatic synthesis of interactive programs (strategies) from declarative specifications expressed in temporal logic. In this course, we show how temporal synthesis provides a practical foundation for strategic reasoning in autonomous AI systems. The key to this research path lies in the rich body of concepts developed in reasoning about actions and planning, combined with a precise treatment of nondeterministic environments and temporal objectives. In such settings, plans must be treated as strategies rather than being blurred with individual execution traces, and goal satisfaction evolves during execution rather than being reducible to reaching states with fixed properties, as in classical planning. These features are naturally captured within the temporal synthesis framework. Technically, we study the structure of the entire space of strategies satisfying a specification. Rather than assuming strategies to be observable, we focus on characterizing the set of execution traces that are compliant with a given strategy space. This perspective provides formal support for guardrailing and responsibility attribution in systems composed of multiple, independently acting autonomous agents.

Lecture 1/3 “On the impact of data for vision-language learning (1/2)”

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Lecture 2/3 “On the impact of data for vision-language learning (2/2)”

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Lecture 3/3 “Building frontier visual segmentation models”

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Lecture “Agentic AI part 1”

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Lecture “Agentic AI part 2”

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Lecture “Agentic AI part 3”

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Lecture 1/2 “Agentic Security (how to attack and defend against prompt injection)”

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Lecture 2/2 “Agentic Security (how to attack and defend against prompt injection)”

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Lecture 1/2 TBA

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Lecture 2/2 TBA

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Lecture 1/3: “Machines That Prove: Foundations of AI for Formal Methods and Mathematics”

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Lecture 2/3: “Machines That Reason: Foundation Models and the State of the Art in AI for Mathematics”

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Lecture 3/3: “Machines That Discover: The Future of Automated Mathematics”

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Lecture 1/3 “The rise of Tabular Foundation Models”

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Lecture 2/3 “TabICL: on open Tabular Foundation Model”

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Lecture 3/3 “Next frontiers for TFMs”

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Lecture 1/3 TBA

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Lecture 2/3 TBA

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Lecture 3/3 TBA

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Lecture 1/3 TBA

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Lecture 2/3 TBA

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Lecture 3/3 TBA

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Lecture 1/3 “Complex Event Recognition/Forecasting and Applications”

Complex Event Recognition (CER) aims to identify complex, time-evolving situations of interest by detecting temporal and relational patterns in continuously arriving event streams, while Complex Event Forecasting (CEF) seeks to anticipate whether and when such situations will unfold, from early signs. In this talk we introduce the fundamental concepts and challenges of CER/F and present formal approaches based on computational logic and temporal formalisms, such as the Event Calculus and symbolic automata. We will examine how complex event patterns are represented, how they are efficiently matched against high-volume streams, and how they can be combined with probabilistic sequence models to produce forecasts and estimates of their expected occurrence time. The methods will be illustrated through applications such as maritime situational awareness, human activity recognition, transport/mobility monitoring and personalized medicine, demonstrating how CER/F transforms heterogeneous streaming data into timely, actionable knowledge.

Lecture 2/3 “Large-Scale Probabilistic Complex Event Recognition/Forecasting”

Real-world event streams are characterized by noisy perception and epistimic uncertainty, making purely crisp event inference insufficient. In this talk we will present probabilistic approaches to CER/F that associate confidence values with both incoming events and the complex situations inferred from them. We will discuss probabilistic extensions of logical temporal models and explain how possible-world inference can be implemented efficiently through knowledge compilation and weighted model counting. By compiling recurring reasoning structures into reusable computational circuits and by expressing temporal inference through parallel tensor operations, these approaches retain declarative temporal semantics while supporting scalable online data processing under uncertainty. We will also present probabilistic forecasting methods that combine symbolic event patterns with learned stochastic sequence models to estimate both the likelihood and the expected timing of future complex events.

Lecture 3/3 “Neuro-Symbolic Reasoning and Learning for Complex Event Recognition/Forecasting”

In the last part of the course we will explore how symbolic temporal reasoning and neural learning can be combined to develop CER/F systems that learn from raw data while retaining structured, interpretable models of complex situations. First, we will consider architectures in which neural perception modules detect low-level events from video or sensor streams, while differentiable temporal logic models compose them into complex events. In this setting, the symbolic model acts as a “teacher” for neural perception: supervision available only for high-level complex events can propagate through temporal reasoning to train the underlying perception modules. We will then move beyond predefined event patterns and present neuro-symbolic methods for learning their structure from labelled sequences, including the induction of executable temporal rules and symbolic automata. These approaches combine the adaptability of neural learning with the compositionality, explainability and formal semantics of symbolic CER/F. Finally, we will discuss neuro-symbolic formal verification: establishing whether perturbations of the raw input can propagate through both the neural and symbolic components and alter system-level conclusions or violate safety properties. We will present scalable relaxation and bound-propagation techniques that can provide sound robustness certificates for the complete perception-reasoning pipeline.

Impact of AI and Optimization on the economics of sustainability