Scholar
Lorenzo Bertolini
Google Scholar ID: axy-mJsAAAAJ
European Commission, Joint Research Centre (JRC)
Citations & Impact
Citations
78
H-index
4
i10-index
2
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- - September 2025: Presenting 4 papers at the 11th International Conference on Machine Learning, Optimization, and Data Science (LOD 2025)
- - July 2025: Presenting a paper on Task-level XAI for biological sex biases in the medical domain at the Gender Bias in NLP (GeBNLP) workshop at ACL 2025
- - July 2025: Paper on conformal prediction for nodule detection accepted for publication at COPA 25 conference
- - June 2025: Work titled 'Dreams are more “predictable” than you think' accepted for publication
- - May 2024: Paper 'On Constructing Biomedical Text-to-Graph Systems with Large Language Models' accepted for publication at EWCS' KNOWLEDGE GRAPH GENERATION FROM TEXT (TEXT2KG) workshop
- - April 2024: Invited talk at the CBS Research Seminar of The Hong Kong Polytechnic University, titled 'Field of dreams: applying NLP to automatize dream content analysis'
- - January 2024: Paper 'Automatic Annotation of Dream Report’s Emotional Content with Large Language Models' accepted for publication at EACL 2024 Computational Linguistics and Clinical Psychology workshop
Research Experience
- - Research Fellow at the European Commission Joint Research Centre (since September 2023)
- - Postdoc in the Artificial Intelligence Research Group, University of Sussex
- - Worked on updates and maintenance of the DReAMy project
Education
- PhD in Informatics, University of Sussex, supervisor not mentioned, specific dates not provided.
Background
- A research fellow (scientific project officer) at the European Commission Joint Research Centre (JRC). His research focuses on explainability and multimodality of AI models for healthcare. Before joining the JRC, he was a postdoc in the Artificial Intelligence Research Group of the University of Sussex, where he also obtained a PhD in Informatics, with a thesis on how LLMs and other machine learning models learn to handle linguistic abilities such as compositionality, generalisation, and inference.
Miscellany
- Interests include but are not limited to applying natural language processing techniques to analyze dream content.
Co-authors