I am currently employed as a Research Associate in Data Science and Machine Learning in the Cardiac Computational Imaging Group under the supervision of Prof Declan O'Regan. My role is integrating medical imaging, genomics and clinical data to unveil clinically relevant subgroups of patients affected by cardiac diseases.
In the recent years, I have worked on designing and applying mathematical models of spatial metabolomics data (mass spectrometry imaging). Specifically, I have been responsible for data acquisition optimisation for DESI-MS imaging and the integration of multi-modal spatial data.
I have been awarded a pre-Bologna Laurea degree (eq. to BSc MSc) in Physics (with specialisation in Theoretical Physics) at the Universita' Degli Studi di Bari, Italy. As member of Prof. Roberto Bellotti group, I have worked on applied machine learning for the automatic segmentation of the human hippocampus from structural MRI scans, as part of the Alzheimer's Disease research programme.
As a PhD student, under the supervision of Prof. Robert C Glen and Prof. Jeremy K. Nicholson, I have worked on robust signal processing pipelines for the pre-processing of DESI-MS imaging data, and the application of unsupervised learning techniques to understand the molecular heterogeneity of cancer.
I have continued my research work on DESI-MS imaging data in Prof. Zoltan Takats group. In particular, I have introduced a molecular colocalization approach for DESI-MS imaging data. During this period, I have also started designing and developing optimisation procedures aimed at integrating quality control and analytical references in DESI-MS imaging experiments, to quantify and disentangle the biological from the technical variability of the measured signals.
During 2020, I have worked at the Francis Crick Institute in Dr Paola Scaffidi lab, where I have applied unsupervised learning on scRNA-seq data to model the functional role of the epigenetic regulatory network in cancer resistance and plasticity.
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et al., 2023, Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia, Cancer Cell, Vol:41, ISSN:1535-6108, Pages:70-87.e14