Scholar
Fabio Mercorio
Google Scholar ID: BpjjTu0AAAAJ
Full Professor in Computer Science, University of Milano-Bicocca
Citations & Impact
Citations
1,921
H-index
23
i10-index
40
Publications
20
Co-authors
32
list available
Publications
Resume (English only)
Academic Achievements
- Paper 'Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs' accepted at IJCAI-25; featured in La Repubblica, Radio24, Canadian HRReporter, Wired, RaiNews24
- Paper 'ITALIC: An Italian Culture-Aware Natural Language Benchmark' presented at NAACL-25; covered by La Repubblica
- Paper on LLM proficiency evaluation using Italian INVALSI benchmark accepted at ECML-PKDD-25
- Paper 'RE-FIN: Retrieval-based Enrichment for Financial data' accepted at COLING-25
- Developed MERLIN: a model-agnostic, global, contrastive explainer for classifiers, published in Decision Support Systems and available on GitHub
- Serving as (Senior) Program Committee member for top-tier conferences including AAAI, IJCAI, EMNLP, NAACL, COLING, ECML-PKDD, CIKM
Research Experience
- Mar 2024–present: Full Professor at University of Milano-Bicocca
- Oct 2024–present: Deputy Director, Department of Statistics and Quantitative Methods, University of Milano-Bicocca
- 2022–present: Director of Master in AI and Data Analytics for Business, University of Milano-Bicocca
- 2010–present: Member of Scientific Committee, CRISP Research Centre, Milan
- Dec 2021–Feb 2024: Associate Professor at University of Milano-Bicocca
- 2020–Aug 2023: Deputy Director of CRISP Research Centre
- 2016–Nov 2021: Assistant Professor at University of Milano-Bicocca
- 2011–2016: PostDoc at University of Milano-Bicocca
- 2017–2018: Partner at TabulaeX Ltd (now LightCast), working on BI and Big Data Analytics
- 2015–2016: Visiting Researcher at King’s College London, AI Planning Group, UK
Background
- Full Professor of Computer Science at University of Milano-Bicocca, Italy
- Expertise in Artificial Intelligence and Data Science
- Research interests include: eXplainable AI (XAI), interpretable models, local and global interpretation, symbolic explanation methods, fairness
- Data Science research areas: Big Data Analytics, Ontology Learning, Word Embedding Evaluation, Large Language Models (LLMs)
- Formerly focused on AI Planning, including domain-independent planning, temporal continuous planning, and hybrid discrete-continuous domain planning
