Ornella Vaccarelli
Ornella Vaccarelli is a Senior Research Scientist at iCoSys and the Lead Scientist at SCAI (Swiss Center for Augmented Intelligence), where she pioneers innovative AI solutions across diverse domains. With expertise ranging from computer vision and computational physics to the latest developments in large language models (LLMs), she bridges cutting-edge research and practical application.
Her collaborative projects span from fundamental research at EPFL to developing an LLM-RAG system for the Swiss parliament. Ornella’s work not only advances scientific understanding but also transforms how industry and government leverage AI for informed decision-making.
A prolific researcher, her findings have been published in high-impact journals, and she is a regular speaker at international conferences. Ornella earned her PhD in Computational Physics from Sorbonne University in Paris and holds a Master’s in Theoretical Physics from the University of Bari. Her career exemplifies a commitment to pushing the boundaries of AI while ensuring its responsible and effective integration into real-world applications.
Beitrag
Ever wished your AI could do more than just answer a question?
What if you could teach it how to think, decide, and verify?
This workshop introduces one of the newest and most powerful AI architectures: graph-based reasoning systems, where AI agents follow structured decision graphs to retrieve information, reason step by step, and produce reliable, evidence-based answers.
We begin from first principles, explaining how large language models work and why they cannot be trusted on their own. You will then build a complete RAG pipeline that connects an open-source LLM to real documents, enabling it to retrieve evidence and generate fact-grounded answers instead of confident guesses.
From there, we move beyond linear pipelines.
You will transform your RAG system into a graph-based AI agent: an autonomous assistant whose reasoning is explicit, structured, and controllable. Using a reasoning graph, the AI learns when to retrieve information, how to combine multiple sources, when to verify results, and when to stop. Instead of one-shot responses, your system follows a clear decision flow that mirrors human problem-solving.
By the end of the workshop, you will have built a complete, open-source AI assistant that can read documents, retrieve knowledge, reason through a graph, and answer with evidence.
Not a simple chatbot.
A truly intelligent AI agent that follows a path of thought.