Machine Learning Laboratory

Department of Engineering and Architecture, University of Trieste

Advanced research in machine learning

About Our Laboratory

The Machine Learning Laboratory at the University of Trieste brings together researchers from complementary areas of artificial intelligence and machine learning. Our work spans from evolutionary computation and genetic programming to explainable AI and research on process mining, bridging theory with practical applications.

Under the leadership of Prof. Sylvio Barbon Junior and Prof. Andrea De Lorenzo, we focus on developing interpretable machine learning models, optimizing complex dynamical systems, and creating practical tools for real-world applications. Our approach emphasizes the integration of advanced machine learning techniques with domain expertise to address challenges in business process optimization, digital transformation, and industrial applications.

Our lab fosters a collaborative environment where theory and practice converge, emphasizing mentorship, multidisciplinary collaboration, and the development of tools with tangible impact in industry and research.

Team

Group Leaders

Prof. Sylvio Barbon Junior

Prof. Sylvio Barbon Junior

Associate Professor

Artificial Intelligence, Process Mining, Data Science, Explainable AI, AutoML

Email: sylvio.barbonjunior@units.it

Phone: +39 040 558 3146

Office: Building C3, Room C3_2.14, Floor 2

GitHub: github.com/sbarbonjr

Prof. Andrea De Lorenzo

Prof. Andrea De Lorenzo

Associate Professor

Evolutionary Computation, Machine Learning in Engineering, Computer Security, NLP

Email: andrea.delorenzo@units.it

Phone: +39 040 558 3419

Office: Building C3, Room C3_2.54, Floor 2

Researchers

Azin Moradbeikie

Azin Moradbeikie

Researcher

Researcher 2025, University of Trieste

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Luigi Rovito

Luigi Rovito

Researcher

PhD 2022-2024, University of Trieste

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PhD Candidates

Andrea Sodomaco

Andrea Sodomaco

PhD Candidate

PhD 2025-2027, University of Trieste

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Frederico JJB Santos

Frederico JJB Santos

PhD Candidate

PhD 2024-2026, University of Trieste

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Rafael Gonçalves

Rafael Gonçalves

PhD Candidate

PhD 2025-2027, University of Trieste

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Iuliana Malina Grigore

Iuliana Malina Grigore

PhD Candidate

PhD 2024-2026, University of Trieste

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Davide Tugnoli

Davide Tugnoli

PhD Candidate

PhD 2025-2028, University of Trieste

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Completed PhD Students

Leonardo Arrighi

Leonardo Arrighi

PhD (Completed)

PhD 2023-2025, University of Trieste

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Giovanni Pinna

Giovanni Pinna

PhD (Completed)

PhD 2023-2025, University of Trieste

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Matheus Camilo da Silva

Matheus Camilo da Silva

PhD (Completed)

PhD 2023-2025, University of Trieste

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International Visiting

Mauro Castelli 🇵🇹

Institution: Universidade Nova de Lisboa, Portugal

Year: 2022

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Giovanni Cinà 🇳🇱

Institution: University of Amsterdam, Netherlands

Year: 2023

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Pâmela de Souza Schiaber 🇧🇷

Institution: Universidade Tecnológica Federal do Paraná, Brazil

Year: 2023

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Fetenech Ganebo Maskele 🇪🇹

Institution: Università Wolaita Sodo, Ethiopia

Year: 2024

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Ingrid Alves de Moraes 🇧🇷

Institution: State University of Campinas, Brazil

Year: 2024

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José Vinicius Ribeiro 🇧🇷

Institution: State University of Londrina, Brazil

Year: 2025

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Alan Gonçalves Amaral 🇧🇷

Institution: State University of Campinas, Brazil

Year: 2026

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Research Projects

Decision Predicate Graphs (DPG)

Status: Ongoing

Framework converting tree ensemble models into interpretable graph structures for transparency. Focus on explainable AI and model interpretability.

Research Area: Explainable AI, Model Interpretability

Keywords: Decision Trees, Transparency, XAI

GitHub

PoAC - Problem-oriented AutoML in Clustering

Status: Ongoing

Optimization framework for unsupervised learning. Automated clustering pipeline generation with focus on practical applicability.

Research Area: AutoML, Clustering Optimization

Keywords: Unsupervised Learning, Pipeline Optimization

GitHub

TPOT-Clustering

Status: Ongoing

Extended TPOT AutoML tool enabling automated clustering pipeline generation. Bridges theory with practical implementation.

Research Area: AutoML, Data Science

Keywords: Pipeline Optimization, Clustering

GitHub

ACANCOS

Funding: HORIZON-MSCA-2022-SE-01 | Duration: 2024-2027 | Status: Ongoing

Marie Skłodowska-Curie Staff Exchanges project across five countries focusing on "efficient decision algorithms for complex dynamical systems".

Scope: International collaboration, 5 countries

REPA - AI in Process Mining

Funding: Grant 2022CJWPNA | Status: Ongoing

AI and deep learning applications to process mining and business process optimization. Focus on real-world business impact.

Keywords: Process Mining, Deep Learning, Business Optimization

PORTRAIT - Digital Twin for Port-to-Rail

Funding: PR FESR 2021–2027 | Duration: Sep 2024–Dec 2027

Digital twin technology for port-to-rail intermodal transport in the Adriatic region. Application of ML to industrial logistics.

Application: Digital Twins, Logistics, Intermodal Transport

Publications

Prof. Sylvio Barbon Junior

Access Prof. Sylvio Barbon Junior's complete publication record on Google Scholar:

View Google Scholar Profile

Research Areas: Explainable AI, AutoML, Process Mining, Data Science, Machine Learning

Prof. Andrea De Lorenzo

Access Prof. De Lorenzo's complete publication record on Google Scholar:

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Research Areas: Evolutionary Computation, Machine Learning in Engineering, NLP, Genetic Programming

Contact Us

Location

Department of Engineering and Architecture
University of Trieste
Trieste, Italy

Prof. Barbon

Email: sylvio.barbonjunior@units.it
GitHub: github.com/sbarbonjr

Prof. De Lorenzo

Email: andrea.delorenzo@units.it
Phone: +39 040 558 3419
Office: Building C3, Room C3_2.54