teaching

Courses, tutoring activities, and thesis supervision at the University of Bologna.

2023-2026

Text Mining and Natural Language Processing

A.Y. 2023–2026 Giacomo Frisoni, Gianluca Moro

Adjunct Professor (co-instructor with Gianluca Moro) — Master in Data Science and Business Analytics, Bologna Business School. Machine learning and deep learning for textual data. Text representation techniques: bag-of-words, TF-IDF, word2vec, contextualized embeddings. Processing methods: lexicon-based predictors, multilayer perceptrons, recurrent neural networks, transformers. Large language model prompting and fine-tuning, with a focus on text classification tasks.

Big Data Analytics and Text Mining (Module 1)

A.Y. 2023–2026 Course coordinator — Gianluca Moro

Tutor — M.S. in Artificial Intelligence, University of Bologna. The course introduces students to the fundamentals and state-of-the-art methodologies of Natural Language Processing, actively incorporating recent milestones and newly published research. Content focuses on Large Language Models, covering prompt optimization, model compression and quantization, parameter-efficient fine-tuning, and multimodal language models, as well as approaches for integrating external knowledge such as Retrieval-Augmented Generation, Graph Neural Networks, and agents.


Thesis Supervision

Co-supervisor of 57 Bachelor’s and Master’s theses on Natural Language Processing and Deep Learning at the University of Bologna. A complete list is available on AMS Laurea, the University of Bologna’s official open-access institutional thesis repository.

Selected theses:

  • Buzzoni, M. (2026). Graph-of-Mark: Graph-Based Visual Prompting for Enhanced Spatial Reasoning in Multimodal Language Models. M.S. thesis, Artificial Intelligence.
  • Raponi, M. (2025). Multimodal Generative Information Retrieval of Chest X-Rays Grounded on ICD-9 Taxonomy. B.S. thesis, Computer Science and Engineering.
  • Zecca, A. (2024). End-to-End Extraction and Injection of Graphs: A Self-Supervised Neuro-Symbolic Method for Explainable Large Language Models. M.S. thesis, Artificial Intelligence.
  • Monaldini, N. (2024). Large Action Models: End-to-End Retrieval-Enhanced Learning for Generating Function Calls from Instruction Manuals. B.S. thesis, Computer Science and Engineering.
  • Cocchieri, A. (2023). Leveraging Large Language Model Distillation to Enhance Zero-Shot Named Entity Recognition and Classification. M.S. thesis, Artificial Intelligence.
  • Presepi, A. (2023). To Generate or to Retrieve: On the Effectiveness of Artificial Contexts for Biomedical Question Answering. B.S. thesis, Computer Science and Engineering.

International Thesis Evaluation Commissions

  • Universidad Técnica Federico Santa María (UTFSM), Valparaíso, Chile (2026) — External Member of the Thesis Committee for the Magíster en Ciencias de la Ingeniería Informática; written evaluation of the dissertation and participation in the oral defense (topics: counterfactual explanations, explainable AI, NLP).