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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.
Download CV in pdf format


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.

Work Experience

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.