About Giovanni Stilo

Biography

Giovanni Stilo is an Associate Professor in the Department of AI, Data and Decision Sciences at Luiss Guido Carli International University and a Core Faculty member of Luiss Business School, where he serves as Scientific Director of the Major in Applied Artificial Intelligence for Business. He is the founder and coordinator of the AIIM (Artificial Intelligence & Information Mining) Research Collective, an interdisciplinary group of researchers sharing a common interest in AI, Data Mining, and Machine Learning.

Previously, he served as Associate Professor at the University of L’Aquila (2018–2025), where he also coordinated the Master’s Degree Program in Applied Data Science (2021–2025). From 2012 to 2018 he held research positions at Sapienza University of Rome, and in 2014 he was a Visiting Researcher at Yahoo Labs in Barcelona, Spain.

In 2024, he was recognized as one of the Top 500 Most Influential Italians in Artificial Intelligence.

Education

Career Timeline

• Sept 2025 – present: Associate Professor & Core Faculty, Luiss University / Luiss Business School; Scientific Director, Major in Applied AI for Business
• Dec 2021 – Aug 2025: Associate Professor, University of L’Aquila; Head of the M.Sc. in Applied Data Science (2021–2025)
• Dec 2018 – Dec 2021: Assistant Professor (Tenure Track), University of L’Aquila
• Jul 2016 – Dec 2018: Assistant Professor, Sapienza University of Rome
• 2014 – 2016: Post-doctoral Research Fellow, Sapienza University of Rome
• May – Nov 2014: Visiting Research Fellow, Yahoo Labs, Barcelona, Spain (Web Mining group)
• 2013 – 2015: Research Contracts, Pediatric Hospital Bambino Gesu (e-Health data mining)
• 2010 – 2012: Assistant Professor (Short Term), “Tor Vergata” University of Rome
• 2009 – 2013: Ph.D. Student, University of L’Aquila
• 2009 – 2012: Applied Research Fellow, Nestor s.c.a.r.l. Laboratory (Information Retrieval)
• 2007 – 2008: Project Contracts, Ministry of Justice (SIDIP/DIGIT evaluation)

Awards & Honors

• AI Consultant, Italian Data Protection Authority (GPDP) – Winner of the comparative procedure for AI consultancy contracts
• National Scientific Qualification – Full Professor (Sector 09/H1 comp.-eng.), 2023–2034
• National Scientific Qualification – Associate Professor (Sectors 01/B1 comp.-science and 09/H1 comp.-eng.), 2019–2028
• Top 500 Italians Who Matter in AI – Featured by Repubblica (2024)
• Senior Program Committee Recognition – CIKM 2024
• Best Student Paper Award – CSCWD 2017 (“Detecting Network Leaders in Enterprises”)
• TREC 2008 – 2nd Place in the Legal Track, NIST, Gaithersburg, Maryland
• Degree Award by Confindustria and A.I.C.A. for best works in I.C.T. research (2009)
• Ministry of Cultural Heritage Expert – Included in the official list of experts for development and management of computer applications (2011–2013)
• IEEE Member since 2016

International Collaborations & Visiting

Visiting Positions:

• Technical University of Munich (Jul 2024) – Hosted by Prof. Gjergji Kasneci, collaboration on Responsible and Trustworthy AI
• University of Havana, Cuba (Dec 2019 – Jan 2020) – Visiting Professor, collaboration on topic detection and discovery
• Yahoo Labs, Barcelona (May – Nov 2014) – Visiting Researcher, Web Mining group led by Prof. Ricardo Baeza-Yates

International Cooperation Agreements:

• University of Havana, Cuba – Established 2013, renewed March 2024. Supervised 10+ visiting students over the years
• George Mason University, USA – Established March 2023. Collaboration on anomaly detection, social network mining, and multiplex networks (with Prof. Carlotta Domeniconi)

Research Collaborations:

• CLAIRE (Confederation of Laboratories for AI Research in Europe) – COVID-19 Task Force, bioinformatics and molecular data analysis

Research Focus

Prof. Stilo’s research spans several areas of AI and Machine Learning, with a primary focus on addressing real-world problems that require large-scale data processing:

• Graph Counterfactual Explainability (GCE): Developing methods and frameworks (GRETEL, RSGG-CE) to explain Graph Neural Network decisions through counterfactual reasoning. Published at AAAI, ACM Computing Surveys, WSDM, ECML-PKDD, CIKM, and IJCAI.

• Machine Unlearning: Research on selective data removal from trained models (ERASURE framework, Forget-Set Identification problem), addressing GDPR compliance and responsible AI. Published in Machine Learning (Springer) and at CIKM/IJCAI.

• Algorithmic Fairness & Bias: Founding the BIAS workshop series at ECIR (2020–2023), developing debiasing techniques for classification (Debiaser, IPM), and studying gender gaps in academia and enterprise social networks (JSS, Social Networks).

• Health Informatics & Computational Biology: Syndromic surveillance from social media (AIIM, PLoS One, BMC Infectious Diseases), drug repurposing via deep graph networks (IEEE TETC), disease-gene prediction (BIBM, SAC), and contributing to the CLAIRE COVID-19 Task Force.

• Temporal & Social Mining: Efficient temporal mining of micro-blog texts for event discovery (DMKD), hashtag sense clustering (Computational Linguistics), topic detection in news providers (IDA), and buzzing story discovery in anomalous temporal graphs (WIJ).

• Recommender Systems & User Profiling: Semantic recommendation for micro-blog users, automatic acquisition of user interest taxonomies (Web Semantics), multi-domain interest datasets (Wiki-MID at ISWC), topic recommenders for journalists (IRJ), and enterprise social network analytics (ICWSM, CSCWD).

Scientific Community Service

Active in editorial boards, conference organization, and program committees at top AI/ML venues. See the full Service page for details.

Highlights: PC Co-Chair of CIKM 2025, Workshop Chair of WSDM 2026, Associate Editor of KAIS (Springer), Senior PC at ECAI/IJCAI/CIKM/SDM, editor of 5 Springer volumes, reviewer for TKDD/TKDE/AI/DAMI and more.

The AIIM Collective

The Artificial Intelligence & Information Mining (AIIM) collective – pronounced as “I’m” (/aɪm/) and “aim” (/eɪm/) – brings together individuals who share a common interest in AI, Data Mining, and Machine Learning. The collective emphasizes open science, reproducibility, and responsible AI development.

Visit the AIIM collective at aiimlab.org.

Contact

• Email: gstilo@luiss.it
• Google Scholar: Profile
• GitHub: aiim-research
• Academic CV: Download PDF