Research Summary

My main research interests concern Biomedical Natural Language Processing (BioNLP), Knowledge Base Construction (KBC), and Federated Learning.

In the domain of BioNLP, a promising line of research I plan to pursue in the future regards Event Extraction.
A brief introduction is available at this presentation: Event Extraction Presentation

I am part of the UNIPD research unit in the HEREDITARY H2020 EU project (more information below).

My main research contributions are within the fields of "Information Retrieval" and "Information Extraction".

Interests

  • Biomedical Natural Language Processing
  • Knowledge Base Construction
  • Federated Learning
  • Ontologies
  • Data Models and Structures

Active Projects

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    HEREDITARY

    2024 - 2027

    HetERogeneous sEmantic Data integratIon for the guT-bRain interplaY

    HEREDITARY aims to significantly transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. The HEREDITARY framework comprises five interconnected layers, from federated data processing and semantic data integration to visual interaction.

    By utilizing advanced federated analytics and learning workflows, we aim to identify new risk factors and treatment responses focusing, as exploratory use cases, on neurodegenerative and gut microbiome related disorders. HEREDITARY is harmonizing and linking various sources of clinical, genomic, and environmental data on a large scale. This enables clinicians, researchers, and policymakers to understand these diseases better and develop more effective treatment strategies. HEREDITARY adheres to the citizen science paradigm to ensure that patients and the public have a primary role in guiding scientific and medical research while maintaining full control of their data. Our goal is to change the way we approach healthcare by unlocking insights that were previously impossible to obtain.

    Role: Participant

    Project No: 101137074
    Call: HORIZON-HLTH-2023-TOOL-05
    Topic: Tools and technologies for a healthy society
    Funding (UNIPD): 1.138.046€
    Website: https://hereditary-project.eu/

Past Projects

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    BITSEI X COVID IN LA

    2023 - 2024

    Leveraging open data to understand the socio-economic effects of the CoVid pandemic on the city of Los Angeles, USA.
    Source Code: GitHub Repository.
    Topic: Knowledge Base Construction

    This project has been carried out for the course of "Graph Databases" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.
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    BITSEI

    2023

    Business InTegrated System for Electronic Invoicing
    A web app designed to help companies and professionals to manage their business activities efficiently while being compliant with Italian regulations.

    Source Code: GitHub Repository.
    Topic: Web App Development.

    This project has been carried out for the course of "Web Applications" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.
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    CLOSE @ CLEF 23

    2023

    Clef Long Eval X Search Engines
    An information retrieval system that can effectively handle changes over time, specifically focusing on the temporal evolution of Web documents.
    This system has been developed to participate in the LongEval Lab at CLEF 2023, Task 1: LongEval-Retrieval.

    Source Code: GitHub Repository.
    Paper: Click Here.
    Topic: Information Retrieval.

    This project has been carried out for the course of "Search Engines" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.