Studenti che ho supervisionato o co-supervisionato.

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Studenti di Laurea magistrale e triennale

Università di Trento:

• Juana Sofia Cruz Contento, “Comprehensive analysis of predictive models for determining hospital length of stay after coronary artery bypass surgery“ (MSc, 2025)
• Silvia Menchetti, “Generative AI for synthetic omics data” (MSc, 2024)
• Federica Amore, “Evaluation of machine learning pipelines to predict lung cancer survival from gene expression and clinical data” (BSc, 2024)
• Adriano Voltolini, “Multimodal survival prediction in lung adenocarcinoma patients” (MSc, 2023)
• Federica Rignanese, “Deep learning for survival prediction of prostatic cancer patients from PET/CT imaging” (MSc cum laude, 2023)
• Elisa Paolazzi, “Enhancing reproducibility and interpretability in Crohn’s disease and ulcerative colitis detection” (MSc cum laude, 2023)
• Tobia Pavona, “Efficient Python implementation of Integrative Network Fusion” (BSc, 2023)
• Tamara Rigo, “Artificial Intelligence for improving the diagnosis and treatment of cardiovascular diseases: a case study on a large longitudinal Italian cohort” (MSc cum laude, 2022)
• Giacomo Bornino, “Machine Learning prediction of diabetes comorbidities in a large Italian cohort” (MSc cum laude, 2021)
• Nicolae Puica, “AI in predictive diagnostics: deep learning for gastrointestinal endoscopic imaging in Inflammatory Bowel Diseases” (MSc, 2021)
• Federica Ress, “Bioinformatics and machine learning for single-cell transcriptomics” (BSc, 2020)

Università di Milano:

• Samer Kharboush, “Machine learning-based techniques for coronary artery disease prediction in a precision medicine setting” (MSc, 2022)

Studenti di dottorato

Federica Rignanese (University of Trento, PhD in corso)

Alessandro Zandonà, “Predictive networks for multi meta-omics data integration” (Università di Trento, PhD cum laude, 2017)

Tirocinanti

• Gianmarco Midena (ora dottorando all’Università di Aalto)
  Deep learning applied to radiogenomics for the prediction of immune checkpoint inhibitor response in lung cancer patients (2021)
• Eva Viesi (ora dottoranda all’Università di Verona)
  Implementation of a Python-based bioinformatics pipeline for data exploration and downstream analysis of single-cell sequencing expression matrices using public COVID-19 data (2021)
• Marco Giulini (ora postdoc all’Università di Utrecht)
  Machine learning methods for predictive toxicology (2018)