Current Position

2019 - Ongoing PhD Researcher in Machine Learning for Precision Medicine Tubingen, Germany Max Planck Institute for Intelligent Systems

Part of the Marie Curie Innovative Training Network entitled “Machine Learning Frontiers in Precision Medicine”
Collaborated with international groups of experts from a variety of scientific domains, which led to the development of multidisciplinary skills.
Designed and implemented deep-learning-based models and probabilistic models to solve problems in biology and biomedicine.
Gained expertise with several types of biological data, including sequencing data, proteomics, mass spectra, clinical records, molecular networks, chemical structures.
Published as first author or shared first author in internationally renowned journals, including Nature Communications, Bioinformatics, and Briefings in Bioinformatics.
Gained mentoring experience, helping supervise Master students. Part of the MAXMINDS mentoring network to help disadvantaged students affected by the 2023 earthquake in Turkey and Syria.
Supervised by Prof. Bernhard Schölkopf and Dr. Gabriele Schweikert.

Skills

Technical Skills

Machine Learning and Statistics: Deep learning, Transformers, LLMs, Linear algebra, Bayesian modeling, Explainable AI, Graph Neural Networks, Network Analysis, Conformal inference hypothesis testing, MLflow.
Python: PyTorch, NumPy, Pandas, scikit-learn, networkX, statsmodels, PyMC, FastAPI, Flask.
Data Manipulation and Visualization: SQLite, HDF5, Interactive visualizations, Data mining, Exploratory data analysis.
R Programming: Tidyverse, caret, tidymodels, Bioconductor.
Software Development: Git, Github, Docker.
Specialized Software: RDKit, BLAST, MMSeqs2.

Experience with the following biomedical topics and data types

Antimicrobial resistance and clinical pathology: MALDI-TOF spectrometry, drug resistance outcomes.
Clinical oncology and cancer biology: clinical data of cancer patients, somatic mutation profiles, TCGA database.
Epigenetics: histone modifications (ChIP-seq), DNA methylation (BS-Seq), chromatin accessibility (ATAC-seq, DNase-seq), ENCODE and Roadmap Epigenomics databases.
Proteins: UniProt and UniRef databases, LLMs for proteins, AlphaFold.
Omics data: scRNA-seq, RNAseq.
Immunology and immunopeptidomics: MHC class I pathway, HLA alleles, pMHC complexes, immunoglobulin structure, IEDB database.
Representations of small molecules: SMILES, chemical fingerprints, graph-based representations, ChEMBL database.

2017 - 2018 Junior Developer and Consultant Padova, Italy Espedia Consulting

Contributed to the creation of customized software solutions for clients, prioritizing robustness in design and ensuring on-time delivery.
Applied object-oriented principles and design patterns to create scalable and maintainable code in Python and JavaScript.
Developed presentations and proposals by synthesizing data and insights into actionable recommendations.

2018 - 2019 MSc in Artificial Intelligence Edinburgh, Scotland University of Edinburgh

Master of Science with a focus on machine learning and deep learning.
Graduated with Distinction.
Thesis: "Optimising Recommendation Slates Using Deep Determinantal Point Processes"
Supervisors: Dr. Roberto Pellegrini and Aleksandr Petrov.

2014 - 2016 Master's Degree in Physics Trento, Italy University of Trento

Master's degree in experimental physics with a focus on medical physics.
Graduated with 110/110 marks with honours.
Thesis: "Polymer Templating of Porous Silicon for Drug Delivery Applications"
Supervisor: Dr. Paolo Bettotti.

2012 - 2014 Bachelor's Degree in Physics Torino, Italy Università di Torino

Graduated with 110/110 marks with honours.
Thesis: "Modelization of Nano Amplified Targeted Therapy (nATT)"
Supervisor: Prof. Cristiana Peroni
Collaborator: Dr. Andrea Attili.