The project aims at the development of a shared technological infrastructure for studying age-related diseases. The project has
been funded through a grant of the Italian Ministry of Health (Convenzione Progetto di Rete), and involves several research
hospitals in Italy (Principal Investigator: INRCA Ancona). Since 31/12/2023 I'm the team leader of IRCCS Ospedale Galeazzi
Sant'Ambrogio for machine learning and data science activities, as well as for coordination with other institutes involved
in the project.
softpy
softpy is a Python library for soft computing (in particular, fuzzy systems and evolutionary computing),
compatible with (and based on) scikit-learn.
I'm the primary developer and mantainer of the library, which was developed as a way to teach Bachelor-level students the
principles of soft computing, within the context of the Fuzzy Systems and Evolutionary Computing of the Bachelor degree in
Artificial Intelligence at the University of Milano-Bicocca. The library has since evolved into a general-purpose library, albeit
still with an educational/academic (not production-ready) scope.
The library is open-source and available on GitHub.
The library currently focuses on implementations of fuzzy sets (both discrete and continuous), operations, fuzzy control systems
as well as general-purpose meta-heuristic algorithms (mainly population-based).
scikit-weak
scikit-weak is a Python library for weakly supervised learning, compatible with (and based on) scikit-learn.
I'm the primary developer and mantainer of the library, which also feature contributions by
Julian Lienen from Paderborn University.
The library is open-source and available on GitHub and
PyPi.
The library currently focuses on learning from imprecise data, self-supervised learning
and label regularization problems, by providing a framework for management and representation of weakly supervised data,
state-of-the-art classification algorithms, as well as pre-processing, data management, and evaluation utilities.
DSS Quality Assessment
DSS Quality Assessment is a Web Tool for the assessment of data-driven decision support systems, especially those based on AI and ML methodologies.
The Web Tool encompassess different functionalities to provide a holistic approach to quality assessment, in particular it allows to:
evaluate the similarity of datasets, evaluate the calibration and robustness of decision support systems, as well as evaluate the utility and impact
of a decision support system on human decision-making. The web tool was developed at the MUDI Lab of the University of Milano-Bicocca and draws from
multiple years of research conducted at the same lab. The web tool is available here.
COVID-19 Blood-ML
The aim of this project was to develop diagnostic and prognostic models for COVID-19 disease, based on
blood test data. The project has led to the collection of multiple datasets from several hospitals and clinics
around 4 countries (Italy, Spain, Brazil, Ethiopia), which are publicly available (for benchmarking, analysis and model
development purposes) on Zenodo:
