REGISTER HERE

Workshop Title
Explainable Artificial Intelligence over Knowledge Graph: From Reinforcement Learning to Generative Modeling

Time: Monday - Friday, 9:00 AM - 11:00 AM, daily

Five-Day Workshop Aim and Scope
Intelligent systems shape many daily decisions, but their reasoning is often unclear. Among them, Recommender Systems personalize content but need transparency to build trust. This course explores how Knowledge Graphs (KGs) can enhance explainability by structuring knowledge and supporting reasoning. Participants will integrate KGs with Reinforcement Learning and Generative Models, using tools like HuggingFace and Hopewise in hands-on sessions to build explainable recommender solutions.

What you will learn

• Enhance model explainability by integrating Knowledge Graphs into intelligent systems
• Use Large Language Models and Reinforcement Learning to provide explainable recommendations
• Understand how Transformers and Large Language Models work, including their core architecture and mechanisms
• Train and evaluate recommender systems with HuggingFace and Hopewise libraries

Who is it for
University students, faculty, community members, and industry professionals who are interested in building intelligent systems that are not only effective but also transparent and explainable.

What you need
No prior experience with Knowledge Graphs, Reinforcement Learning, or Large Language Models is required. Just bring your curiosity and a laptop — everything else will be provided during the course through interactive notebooks.

Speaker
Dr. Francesca Maridina Malloci
Assistant Professor, University of Cagliari
Department of Mathematics and Computer Science (Italy)

Francesca Maridina Malloci is Assistant Professor at the Department of Mathematics and Computer Science of the University of Cagliari (Italy). Her research focuses on responsible artificial intelligence, with attention to decision-making systems, such as recommender systems, for multi-stakeholder contexts. She has co-authored papers in international journals and conferences. Francesca has chaired workshops and tutorials, including those on knowledge discovery at ACM UMAP,ECML-PKDD, and AIED, on explainable AI at ECIR, and on bias and fairness at SIGIR. She also serves as Associate Editor for Neural Processing Letters (Springer) and Information Processing in Agriculture (Elsevier).

Transportation and Parking

https://www.boisestate.edu/coen-cs/currentstudents/aboutccp/#park