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Alma universitas studiorum parmensis A.D. 962 - Università di ParmaAlma universitas studiorum parmensis A.D. 962 - Università di Parma People and Organization
Università degli Studi di Parma

Giovanni Saraceno

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Fixed-term researcher
Department of Economics and Management
SSD: Economic Statistics (STAT-02/A)

Contacts

E. giovanni.saraceno@unipr.it
Office: Polo Kennedy - Plesso Centrale di Economia
Address: Via John Fitzgerald Kennedy 6, 43125 Parma

Office hours are by appointment. Please contact me by email to arrange a meeting, either in person or online.

About

Giovanni Saraceno is a tenure-track researcher (RTT) in the Department of Economics and Management at the University of Parma.

His work focuses on statistics and data science, with a particular interest in developing reliable methods for analysing complex data. His research covers robust statistics, statistical distances and machine learning, with applications in biomedicine, neuroscience and environmental science.

Alongside his research, he teaches statistics, data science and data management, and develops statistical software in R, Python and MATLAB.

Curriculum Vitae

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Since September 2026, Giovanni has been a tenure-track researcher (RTT) in the Department of Economics and Management at the University of Parma. Previously, he was a fixed-term assistant professor (RTD-A) in the Department of Statistical Sciences at the University of Padua, from October 2024 to August 2026, and a Postdoctoral Research Associate in the Department of Biostatistics at the University at Buffalo, USA, from November 2022 to August 2024.

He received the PhD in Mathematics with honours from the University of Trento in 2022, with a thesis on robust estimation under casewise and cellwise contamination. He obtained the master’s degree in Mathematics from the University of Trento in 2017 and the bachelor’s degree in Mathematics from the University of Palermo in 2015.

During the academic training, he undertook research visits at the Indian Statistical Institute in Kolkata and the Knowledge Engineering and Discovery Research Institute at Auckland University of Technology, working on robust estimation methods and brain-inspired neural networks, respectively.

In the 2026/2027 academic year, he teaches Introduction to Data Science, Statistics for the Digital Economy, and Data Management for Sustainability Assessment at the University of Parma. Previously, he taught Mathematics and Statistics in the bachelor’s programme in Biotechnology and Statistics for Neuroscience in the PhD programme in Neuroscience at the University of Padua.

He contributes to the development of open-source statistical software as the maintainer of the R package QuadratiK and an author of the Python package QuadratiK and the R package robustsur.

Teaching

Research

Research areas

The research focuses on developing statistical methods for analyzing complex data, with particular attention to robustness against outliers and data contamination. His work combines the study of theoretical properties, computational implementation and applications to real-world problems.

The main research areas include:

• Robust statistics: developing estimation and outlier detection procedures for multivariate data, regression models and mixed-effects models, addressing contamination affecting either entire observations or individual data entries.

• Statistical distances and hypothesis testing: studying methods based on quadratic distances and kernels for goodness-of-fit testing and comparisons between two or more populations, with a focus on the sensitivity and robustness of the tests.

• Directional and spherical data: developing estimation, testing and clustering methods for data defined on circles, spheres and multidimensional tori, with applications including climate zone classification.

• Machine learning and neural networks: studying loss functions based on the Hellinger distance for generative adversarial networks, and clustering methods for unsupervised medical image segmentation.

• Applications to neuroscience and health: analyzing biomedical data and developing robust procedures for detecting artefacts in EEG signals.

Publications

Full list of publications