Agostino Gnasso

Ph.D. in Economics
Researcher in Explainable AI, Machine Learning, and Statistics

I hold a Ph.D. in Economics from the University of Naples Federico II, where I specialize in an interdisciplinary blend of Statistical Analysis, Machine Learning, and Economics. My academic journey is rooted in the pursuit of clarity and effectiveness within the rapidly evolving field of data science.

Research Focus

My research primarily explores the interpretability of algorithms, specifically in the field of Explainable Artificial Intelligence (XAI). As machine learning models become increasingly integral to critical decision-making in sectors like finance and healthcare, the necessity for transparency is paramount. My work aims to make these powerful tools more understandable and accessible, with a keen interest in decision-tree methods appreciated for their simplicity and ease of interpretation.

Affiliations & Contributions

I am currently a member of the Bibliometrix Research Team and the academic spin-off K-Synth. Recently, I worked as a Visiting Scholar at the Department of Methodology and Statistics at Leiden University (Netherlands).

My contributions have been recognized in publications in several international scientific journals. Alongside a talented team of colleagues, I actively work on developing innovative open-source software packages for the R programming environment.

Latest News

September 2025 — Three papers accepted at CLADAG 2025 and RC33 2025 in Naples, covering XAI interpretability and health survey analytics.

July 2025 — New release of e2tree v0.2.0 on CRAN, with XGBoost support and regression extensions.

2025 — New article published in Annals of Operations Research: “Predicting depression in Italy using random forest through the E2Tree methodology”. DOI

2025 — New article published in Applied Stochastic Models in Business and Industry: “Extending Explainable Ensemble Trees to Regression Contexts”. DOI

Research Interests

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Explainable AI (XAI)

Making black-box models transparent through interpretable tree structures and visual explanations.

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Machine Learning & Ensembles

Random Forests, XGBoost, and ensemble methods with a focus on interpretability.

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Bibliometrics & Science Mapping

Quantitative analysis of scientific literature, citation networks, and research trends.

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Statistical Software (R & Python)

Open-source package development for data analysis, XAI, and bibliometric research.

Contacts

If you want to get in touch or discuss a potential collaboration:

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You can find me also on LinkedIn, GitHub, Google Scholar and ResearchGate.

Where

I am based at the Monte Sant’Angelo academic complex in Naples.

Department of Economics and Statistics
University of Naples Federico II

📍 Address: Monte S. Angelo, Via Cinthia, 80126 Napoli, Italy.
Office: Room D-22, Sector D, 2nd Floor, Building 3.