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General Information

Full Name Francesco Guerra
Date of Birth 21st May 1973
Languages Italian, English, French
Affiliation Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Italy
Office MO-27-01-034, DIEF, via Vivarelli 12, 41125 Modena, Italy

Education

  • 2004
    PhD in Information Engineering
    University of Modena and Reggio Emilia, Italy
  • 2000
    M.Sc. in Information Engineering
    University of Modena and Reggio Emilia, Italy
  • 1992
    Diploma di Maturita Classica
    Liceo Classico "L. A. Muratori", Modena, Italy

Academic Positions

  • 2022 - present
    Full Professor in Information Engineering
    University of Modena and Reggio Emilia, Italy
    • Teaching Software Engineering and Big Data Analysis.
  • 2019
    Visiting Professor
    University of Rijeka, Croatia
  • 2015 - 2022
    Associate Professor in Information Engineering
    University of Modena and Reggio Emilia, Italy
  • 2005 - 2015
    Assistant Professor in Information Engineering
    University of Modena and Reggio Emilia, Italy

Institutional Roles

  • 2022 - present
    President of the Bachelor's and Master's Degree Course Councils in Computer Engineering
    Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Italy
  • 2024 - 2025
    Rector's Delegate for ICT
    University of Modena and Reggio Emilia, Italy
  • 2021 - 2023
    Coordinator of the Computer Engineering and Science Curriculum, ICT Doctoral School
    University of Modena and Reggio Emilia, Italy
  • 2022 - 2024
    Visiting Reviewer for AI Theses
    University of Malta

Research Profile

  • Trustworthy data-centric AI systems connecting models, data, evidence and explanations.
  • Scalable data management and data integration methods for heterogeneous, structured, semi-structured and textual data.
  • Machine learning and deep learning techniques for entity resolution, entity matching and explainable data integration.
  • NLP and LLM systems for fact-checking, evidence grounding, source attribution, table understanding and decision support.
  • Explainable, auditable and reproducible AI pipelines for textual evidence, tables, financial signals, time series and structured records.

Academic Interests

  • Data Integration and Explainable Entity Matching
    • Entity matching, entity resolution, record linkage and data cleaning over heterogeneous sources
    • Machine learning and deep learning techniques for entity matching
    • Explainable entity matching with landmark-based, evidence-based and cluster-based explanations
    • Analysis of BERT-based and transformer-based models for entity matching
    • Data quality, deduplication and interpretable data integration pipelines
  • Scalable Data Management and Structured Data Analytics
    • Scalable techniques for managing, integrating and analysing large structured and semi-structured datasets
    • Discovery and summarization of structured data
    • Application of NLP and machine learning techniques to structured data
    • Integration of textual, tabular and relational evidence in data-intensive pipelines
  • Evidence-grounded NLP and LLMs
    • Claim verification and fact-checking over textual, tabular and structured evidence
    • Evidence retrieval, source attribution and evidence-level explanations
    • RAG and LLM pipelines whose outputs can be inspected, audited and validated by domain experts
    • Table QA and Multi-table QA over complex reports and semi-structured sources
    • Automatic textual and tabular data analysis
  • Explainable and Auditable AI
    • Post-hoc explanations for black-box machine learning models
    • Evidence attribution, feature contribution analysis and model-agnostic diagnostics
    • Counterfactual explanations for interpretability, recourse and fairness auditing
    • Explainable decision-support systems for high-impact domains
  • Semantic Robustness and Red-teaming of Textual Models
    • Adversarial testing of NLP models and LLM-based systems
    • Universal triggers and semantic-preserving perturbations
    • Robustness auditing of fact-checking, RAG and language-based AI pipelines
    • Bias diagnosis and vulnerability analysis in textual models
  • Interpretable Time-series Analysis
    • Interpretable clustering of multivariate time series
    • Novelty and anomaly detection for system health monitoring
    • Forecasting of irregularly sampled multivariate time series
    • Self-supervised learning and decomposition-based methods for time-series forecasting
  • Benchmarking and Reproducible Evaluation
    • Controlled benchmarks for evaluating LLMs on complex evidence
    • Evaluation protocols for textual, tabular, semi-structured and financial data
    • Synthetic and reproducible benchmarks for comparing AI pipelines
    • Stress testing of models under noise, layout variation, missing data and heterogeneous evidence
  • Fairness, Auditability and Responsible AI
    • Fairness-aware machine learning and fair classification
    • Scalable mitigation of fairness harms
    • Auditability of model behaviour across groups and decision settings
    • Transparent trade-offs between accuracy, fairness and operational constraints
  • Financial AI and Decision Support
    • Reproducible benchmarking for stock market prediction and portfolio allocation
    • Integration of heterogeneous financial signals, including prices, macroeconomic indicators, relations and news
    • Evaluation of classification, regression and ranking models for financial prediction
    • Portfolio-oriented evaluation and decision-support workflows
  • Controllable LLM Systems for Education and Expert Workflows
    • LLM systems guided by explicit phases, interaction moves and assessment criteria
    • Intelligent tutoring systems and reading comprehension support
    • Expert-guided workflows for education, training, compliance and decision support
  • Keyword Search on Structured Data
    • Keyword search techniques for relational and multi-table databases
    • Semantic keyword search over structured data sources
    • Search and exploration of structured datasets
  • Semantic Web and Ontology Integration
    • Ontology alignment and integration
    • Semantic data integration
    • Knowledge-oriented data management

Teaching

  • Current teaching
    • Software Engineering
    • Big Data Analysis
  • Main teaching areas
    • Big data management and analytics
    • Text analytics and natural language processing
    • Data integration and data management
    • Software engineering

Selected Projects

  • 2023 - present
    PANACEA
    PRIN 2022
    • AI-based cybersecurity, anomaly detection, intrusion response and explainable decision support.
    • DTALab contributes expertise on explainable AI, anomaly interpretation, evidence-aware analysis and trustworthy decision-support pipelines.
  • 2024 - present
    RESIST0
    PR FESR Emilia-Romagna
    • Digital twins, production resilience, ESG indicators, ESG-washing detection and decision support.
    • DTALab contributes methods for data analysis, explainable AI, ESG indicators and evidence-aware decision-support workflows.
  • 2013 - 2017
    KEYSTONE COST Action - Semantic Keyword Search on Structured Data Sources
    COST Action IC1302
    • Coordinator of the European COST Action on semantic keyword search over structured data sources.
  • 2017 - 2019
    Re-search Alps
    INEA/CEF/ICT/A2016/1296967
    • Research data integration and information systems.

Online Profiles

Institutional page https://unimore.unifind.cineca.it/get/person/090294
DBLP https://dblp.org/pid/g/FrancescoGuerra.html
Personal website https://fguerra73.github.io/