PhD student at University of Geneva and Hes-so Valais (since 2018)

I am a postdoctoral researcher at IBM Research Zurich and at two applied universities in Switzerland: ZHAW and HES-SO Valais. I obtained a Ph.D. in Computer Science from the University of Geneva in December 2021. My research aims at using interpretable deep learning methods to support and facilitate knowledge discovery in bio-medical research. During my Ph.D., I was a visiting student at the Martinos Center, part of the Harvard Medical School in Boston (MA) to focus on the interaction between clinicians and deep learning systems.

Coming from a background of IT Engineering, I was awarded the Engineering Department Award for completing the M.Phil. in Machine Learning, Speech and Language Technology at the University of Cambridge (UK) in 2017. 

Current projects

Interpretable Modelling of Molecular Sub-typing of Colorectal Cancer Patients
Personalised medicine aims at defining patient-specific treatment strategies that are alternative to, and may be less invasive than, traditional chemotherapy. To do so, we need to develop models to obtain patient stratification. My focus is using interpretable deep learning methods to obtain patient stratification based on multi-omics data (e.g. proteomics, genomics, ..) and imaging data.
Human-centric interpretability for histopathologyExplanations of machine learning models should facilitate the deployment of automated systems in the pathology workflow. To do so, explaining black-box models should take into account the requirements and domain ontology of the end-users.
Interpretability for the media industryWith the AI4Media project, we collaborate to make sure that the European values of ethical and trustworthy AI are embedded in future AI deployments.


Ph.D. in Computer Science
(2017 – 2021) University of Geneva (SW);
Thesis: Interpretability of Deep Learning for Medical Image Classification: Improved Understandability and Generalisation

M.Phil. in Machine Learning and Machine Intelligence
(2016 – 2017) University of Cambridge (UK); Engineering Department Award;
Master Thesis: Improved Interpretability and Generalisation for Deep Learning

During my thesis project, I worked on a spatial smoothing loss function that would allow for target-specific regularization for recurrent natural language models.

MSc in Artifcial Intelligence and Robotics
(2015-2016 Interrupted) University of Rome La Sapienza (IT); interrupted at 24/120 credits; A+ grades in Probabilistic Methods for Experimental Data Analysis; AI and Machine Learning; Neural Networks.

I interrupted this MSc to apply to the MPhil in Cambridge, as it suited the best to my academic interests.

BEng. in IT
(2013 – 2015) University of Rome La Sapienza (IT); Laziodisu grant for meritorious students; BCC Award;

Dissertation: Studying the control of non invasive prosthetic hands over large time spans

Skills in brief I speak Italian, English and French. I understand Spanish and can interact at a basic level. I mostly program in Python, PyTorch, Tensorflow and Keras. I have experience with Matlab and C programming.

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