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gianluigilopardo/README.md

Hi there! 👋

I'm Gianluigi, a quantitative researcher and data scientist working across machine learning, risk modelling and economic policy. I'm interested in building models that are not only accurate, but also interpretable and trustworthy enough to inform real decisions.

I work as a senior consultant at Prometeia, where I develop and validate credit risk models for European banks. I also collaborate with the European Central Bank, where I previously worked as a research analyst in the International Policy Analysis Division. Our recent work uses large language models to forecast oil prices and to study how firms' exposure to generative AI relates to their stock returns.

I hold a PhD from Inria and Université Côte d'Azur, where I studied the foundations of machine learning interpretability under the supervision of Damien Garreau and Frédéric Precioso. My thesis received the 2025 Young Researcher Prize from Métropole Nice Côte d'Azur. Before that, I earned an MSc in mathematical engineering and a BSc in applied mathematics from Politecnico di Torino.

Away from work, I'm usually on my bike, hiking or running. Drop me a line if you're ever in Rome or Brienza!

🌐 More about me: glopardo.com

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  1. attention_meets_xai attention_meets_xai Public

    Code for the paper "Attention Meets Post-hoc Interpretability: A Mathematical Perspective", ICML 2024

    Jupyter Notebook 22 3

  2. anchors_text_theory anchors_text_theory Public

    Code for the paper "A Sea of Words: An In-Depth Analysis of Anchors for Text Data", AISTATS 2023

    Python 14 1

  3. fred fred Public

    Faithful and Robust Local Interpretability for Textual Predictions

    Python 9 2

  4. forecast-evaluation forecast-evaluation Public

    A Python library for comparing forecast accuracy between different models using statistical tests.

    Jupyter Notebook 5

  5. hacktheact hacktheact Public

    Hack the Act! is a RAG-based chatbot designed to demystify the European Union AI Act

    Python 5 1

  6. smace smace Public

    Code for the paper "SMACE: A New Method for the Interpretability of Composite Decision Systems", ECML 2022

    Jupyter Notebook 15 1