Marco Chierici

Students

Present and past students I have supervised or co-supervised.

Masters and undergraduate students

University of Trento:

  • Silvia Menchetti, “Generative AI for synthetic omics data” (MSc in progress, 2024)
  • Federica Amore, “Evaluation of machine learning pipelines to predict lung cancer survival from gene expression and clinical data” (BSc in progress, 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)

University of Milan:

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

Federica Rignanese (University of Trento, PhD in progress)

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

Postgrad interns
  • Gianmarco Midena (now PhD student at Aalto University)
    Deep learning applied to radiogenomics for the prediction of immune checkpoint inhibitor response in lung cancer patients (2021)
  • Eva Viesi (now PhD student at University of 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 (now postdoc at Utrecht University)
    Machine learning methods for predictive toxicology (2018)