Health Researcher · IRCCS Sant'Orsola
Physics background, biomedical mission.
Building interpretable machine learning pipelines for clinical microbiology and multi-omics data.
I develop computational methods to predict antimicrobial resistance, discover patient phenotypes, and make sense of high-dimensional omics data. My work spans MALDI-TOF mass spectrometry, multi-omics integration, genomics, and metagenomics, with a focus on interpretability and clinical impact.
I trained as a physicist and moved into biomedical data science to apply quantitative methods to problems with direct clinical impact. My current work focuses on antimicrobial resistance prediction from MALDI-TOF mass spectrometry, multi-centre data harmonisation, computational patient phenotyping, and computational genomics.
I work in the Computational Genomics Unit, part of the Multi-Omics and Health-Care Data Analytics Unit at Sant'Orsola Hospital in Bologna, and collaborate closely with Prof. Gastone Castellani's Physics4MedicineLab. My research combines machine learning with clinical microbiology, pharmacokinetics, and genomics. I care about making models that are accurate but also interpretable, because a prediction without an explanation is just a well-dressed guess.
I write Python, build pipelines, publish open-source tools, and contribute to interdisciplinary projects. The through-line is reproducibility, and a mild obsession with the details that make a result hold up the second time around.
In practice, my days involve writing Python scripts until something either converges or breaks, reading papers about problems I didn't know existed until last week, preprocessing mass spectra that have strong opinions about baseline correction, and discussing model outputs with clinical collaborators, figuring out together when the data is telling a story we hadn't expected.
There's also a fair amount of staring at loss curves, debugging pipelines that worked yesterday, and writing documentation future-me will thank present-me for. There is more thinking than typing involved. The ratio of coffee to output is best left unquantified.
Recent updates - papers, releases, conferences, and visits.
Started as a Health Researcher in the Computational Genomics Unit (SSD Genomica Computazionale) at IRCCS Policlinico di Sant'Orsola (Azienda Ospedaliero-Universitaria di Bologna).
PhD defended at the University of Bologna (Health and Technologies), evaluated Excellent cum laude. Supervisor: Prof. Gastone Castellani.
Started as a Postdoctoral Researcher at the University of Bologna, continuing work on machine learning for clinical microbiology and omics data within the Physics4MedicineLab with Prof. Gastone Castellani.
One paper, one tool, one research thread. The full lists live on their own pages.
Selected paper
Multicentre antimicrobial resistance prediction in Klebsiella pneumoniae from routine MALDI-TOF spectra of clinical isolates, with cross-site validation.
Flagship software
A Python ecosystem for end-to-end clinical AMR pipelines on MALDI-TOF spectra. Three sklearn-compatible packages that chain into a single workflow: preprocess with MaldiAMRKit, harmonise across batches and sites with MaldiBatchKit, classify with MaldiDeepKit. Designed to be modular and reproducible.
Research thread
Predicting antimicrobial resistance from MALDI-TOF spectra and clinical data ahead of culture-based diagnostics, with cross-site harmonisation so that models learn the biology rather than the instrument.
I am always happy to talk about research collaborations and open-source development, but also about ideas, methods, and topics I have not explored yet. I am as interested in learning something new from someone as in starting something new together. If you have an idea, a question, or a dataset that misbehaves, I would be glad to hear about it.
Based in Bologna, Italy