Giuseppe Jurman

Email: giuseppe.jurman@fbk.eu

Giuseppe Jurman is a mathematician, with a PhD in Algebra, working at MPBA on various aspects of computational biology. His main research interests are statistical machine learning, mathematical modeling for high-throughtput data and network analysis. He is also an expert in scientific programming with Python and other computing languages.

Monica Moroni

Email: mmoroni@fbk.eu

Monica is a mathematician with previous research experience in the field of computational neuroscience. Her main research interests are the development and application of machine learning models to clinical data (imaging data, EHR) with the final goal of providing diagnostic and prognostic tools to support clinicians in their job. In her work she is organized and determined. She is passionate about science communication.

Marco Chierici

Email: chierici@fbk.eu

Marco Chierici (PhD Bioengineering, University of Padova) is a senior data scientist at DSH. His main research interests include the integration of artificial intelligence methods (e.g., deep learning) in bioinformatics & computational biology frameworks, leveraging the information of multiple layers such as high-throughput omics and bioimaging data. He is an expert on bioinformatics tools for massive omics data, high-performance computing, and scientific programming in Python and R. A member of the MAQC/SEQC and FANTOM consortiums, Marco co-authored >40 papers in peer-reviewed journals. Further, he runs workshops on bioinformatics and machine learning, and he is involved in teaching activities on statistics, statistical learning, and data visualization at the Data Science M.Sc. at the University of Trento.

Diego Sona

Email: sona@fbk.eu

Diego Sona

Stefano Bovo

Email: sbovo@fbk.eu

Stefano Bovo is a researcher in the DSH group. He is a biomedical Engineer holding a PhD in Neuroscience. His research interests are biomedical imaging (functional and structural MRI, PET/SPECT and CT), transcranial magnetic stimulation (TMS), biosignals (EEG, EMG) and mathematical modeling. He is currently working on AI solutions for clinical purposes based on EHR, medical imaging and digital pathology data.

Shahryar Noei

Email: snoei@fbk.eu

Shahryar is a researcher in the "data science for health" unit. He has an MS in biomedical engineering from the Sharif University of technology and a Ph.D. in cognitive neuroscience from the University of Trento, focusing on the computational role of neural oscillations. His main research interest is to develop and apply machine learning techniques for clinical applications to facilitate the process of diagnosis, prognosis, and explainability.

Nicolò Lazzaro

Email: nlazzaro@fbk.eu

DSH senior researcher, specialized in AI applications to systems biology. Helps coordinating the EU-funded project 3DSecret, aiming to leverage generative models to unravel stochastic patterns in breast cancer metastatic trajectories via multimodal integration of metabolomic, genomic and transcriptomic data.

Lisa Novello

Email: lnovello@fbk.eu

Lisa Novello is a researcher at DSH. With a background in cognitive and imaging neuroscience (PhD at University of Trento), the goal of Lisa’s scientific activity has been developing and advancing new markers of disease. To achieve so, in her previous research activity Lisa explored novel strategies to collect and analyze Magnetic Resonance Imaging (MRI) data, and investigated their relationship with clinical phenotypes. Currently, Lisa’s main interest is the use of AI-based methods for the discovery of new markers of disease, with a focus on ML/DL methodologies integrating data from multiple domains (e.g. digital pathology, radiological imaging, clinical records). Lisa also collaborates with the MRI Methods Group at CIMeC, University of Trento, where she helps investigate imaging markers of aging and Parkinson’s disease.

Jacopo Tessadori

Email: jtessadori@fbk.eu

DSH senior researcher with previous research experience in neuroscience and analysis of biomedical signals, ranging from cell cultures to EEG-based brain-computer interfaces. His main research interests are the development and application of machine learning models to clinical data, mainly imaging, with the final goal of providing diagnostic and prognostic tools to support clinicians in their job.

Flavio Ragni

Email: fragni@fbk.eu

Flavio Ragni is a researcher in the DSH unit, with a background in Cognitive Neuroscience, holding a Ph.D. from the University of Trento and an MS from the University of Bologna. His doctoral research focused on applying machine learning techniques to functional magnetic resonance imaging (fMRI) data. Currently, his work involves leveraging machine learning and deep learning to analyze and integrate heterogeneous clinical data, with the goal of developing advanced models that assist clinicians in diagnosing and predicting the progression of various diseases.

Andrea Zen

Email: azen@fbk.eu

Andrea Zen is a researcher in the DSH group. He holds a Bachelor's degree in Biomolecular Sciences and Technologies and a Master's degree in Quantitative and Computational Biology, both from the University of Trento. He is primarily involved in the European project iCulture where he employs advanced machine and deep learning methods to identify robust seaweed species. His goal is to estabilish a zero-waste value chain that transforms seaweeds into high-value bioactive compounds.

Walter Endrizzi

Email: wendrizzi@fbk.eu

Walter Endrizzi is a PhD student in Computational Biology at FBK and CIBIO (University of Trento), with a background in Computer Science and Computational Biology. His research focuses on leveraging computational techniques to analyze longitudinal clinical data from patients with neurodegenerative diseases. For his PhD, Walter is developing AI-driven methods to make sense of these complex, multi-dimensional datasets, aiming to capture the dynamic progression of disease and contribute to a more data-informed approach to patient care.

Raffaele Marchesi

Email: rmarchesi@fbk.eu

Raffaele Marchesi is a PhD student in Computational Mathematics at FBK and the University of Pavia. He has a background in Data Science and Computer Science. For his PhD he is working on Generative AI for health data, with a focus on synthetic longitudinal clinical data.

Matteo Pozzi

Email: mpozzi@fbk.eu

Matteo Pozzi is a PhD student in Computational Biology working on explainable artificial intelligence (XAI) applied to the healthcare domain. He has a bachelor degree in biology from the university of Parma and a MSc In quantitative and computational biology from the university of Trento in which he worked on the integration of drug's and CRISPR's screening. He is interested in applying machine learning techniques to clinical, omics and imaging data such that the solution can be better understood and trusted by clinicians, biologist and decision makers.

Massimiliano Datres

Email: mdatres@fbk.eu

Massimiliano Datres is a PhD student in Mathematics at FBK and University of Trento. He is currently working on the analytical, stochastic and applicative aspects of Deep Neural Networks with a particular focus on Quantized Neural Networks.

Lucia Filippozzi

Email: lfilippozzi@fbk.eu

Lucia Filippozzi is a PhD student in Mathematics at FBK and the University of Trento. Her research focuses on Nonparametric Bayesian Statistics and Bayesian Neural Networks. She aims to apply these mathematical techniques to address key challenges in health sciences by leveraging the uncertainty estimation provided by the Bayesian framework.

Gianluca Leonardi

Email: gleonardi@fbk.eu

Gianluca Leonardi is a PhD student in Biomolecular Sciences at FBK and CIBIO (University of Trento). He has a background in Molecular Biology and Neurobiology. For his PhD he is working on biological manifolds, with a focus on the application of AI on single cell RNA-seq data.

Federica Rignanese

Email: frignanese@fbk.eu

I am currently enrolled in the joint PhD program in (UniPV, USI, FBK) in Computational Mathematics, Learning and Data Science, which I’m carrying out at the Data Science for Health (DSH) unit of the Bruno Kessler Foundation (FBK), in Trento. My research focuses on the application of AI and machine learning in healthcare, particularly in precision oncology. I am exploring the integration of multimodal data—including radiomics, digital pathology, and other medical data types—to develop more accurate prognostic and diagnostic models.

Damiano Salvaterra

Email: dsalvaterra@fbk.eu

Damiano Salvaterra obtained his Bachelor's degree in Computer Engineering at the University of Padua and is currently pursuing a Master's degree in Computer Science at the University of Trento. He works as a scientific developer in the DSH Unit.

Carlotta Cazzolli

Email: ccazzolli@fbk.eu

Carlotta Cazzolli has a B.Sc. in Computer, Communications and Electronic Engineering from the University of Trento and is currently doing her M.Sc. in Cybersecurity. She is working as a junior scientific programmer in the Data Science for Health unit.

Venet Osmani

Email: vosmani@fbk.eu

Venet is a Senior Researcher in e-health group at Fondazione Bruno Kessler research institute and Lecturer in department of Psychology and Cognitive Science at University of Trento, Italy. Previously worked with CREATE-NET research centre. His research interests are in computational modelling of human behaviour as well as modelling disease trajectories from clinical (EHR) data to: estimate risk of developing chronic diseases and predict outcomes in critical care (ICU).

Marco Di Francesco

Email: mdifrancesco@fbk.eu

Marco di Francesco is a B.Sc student in Computer Science at the University of Trento. He has been working at FBK as a Scientific Developer, focusing on the application of Machine Learning where he developed Artificial Intelligence systems applied to the fields of healthcare and environment.

Erich Robbi

Email: erobbi@fbk.eu

I'm now pursuing a Master of Science in Data Science at the University of Trento. I am a junior researcher and technologist at the DSH, and I am now involved in a project that relates to digital pathology.