Hi there!👋
About🤌
I'm Gianluigi, an applied mathematician with a strong interest in machine learning and computational linguistics.
I recently joined the European Central Bank, where I focus on quantitative methods and natural language processing within the International Policy Analysis Division. Before that, I was a doctoral researcher at Inria and Université Côte d'Azur. My PhD thesis centered on the Foundations of Machine Learning interpretability, supervised by Damien Garreau and Frédéric Precioso. Previously, I got an MSc in Mathematical Engineering and a BSc in Applied Mathematics, both from Politecnico di Torino.
News
14th October 2024: I successfully defended my PhD thesis on the Foundations of Machine Learning interpretability! 🥳
1st October 2024: I started working for the International Policy Analysis Division of the European Central Bank 🇪🇺
Past
July 21-27: In Vienna for ICML 2024
June 2-15: visiting the Julius-Maximilians-Universität Würzburg
May 2024: Our paper Attention Meets Post-hoc Interpretability: A Mathematical Perspective got accepted to ICML 2024! 🥳🥳🥳
February 2024: new preprint! We investigate the relation between attention-based and post-hoc explanations
November 30, December 1: I've been at the 2nd Nice Workshop on Interpretability
November 24: talk to the Maasai seminar
November: new preprint
July 2-7: I presented my work at Journées de Statistique in Bruxelles
June: I served as PC member to the KGML workshop @ECML 2023
April 25-27: I have been in Valencia for AISTATS
January 31: I presented SMACE to the AI4media network in Florence
January 2023: our analysis of Anchors for text data got accepted to AISTATS 2023
November 17, 2022: talk at the 1st Nice Workshop on Interpretability
September 21, 2022: talk at ECML in Grenoble, presenting SMACE
August 21, 2022: talk at the 2-nd Workshop on Explainable and Ethical AI – ICPR 2022 in Montreal
June 2022: SMACE got accepted @ECML, my first conference paper!
April 2022: I attended the Statlearn spring-school
November 2021: I attended the AI & Companies Week
November 19, 2021: talk at the SophIA Summit
November 2021: new preprint available: we propose a new method for the explainability of composite AI systems
October 1, 2021: I started my PhD
Education
Ph.D. in Applied Mathematics, 2024
Inria & Université Côte d'AzurM.Sc. in Mathematical Engineering, 2021
Politecnico di TorinoB.Sc. in Applied Mathematics, 2019
Politecnico di Torino
Experience
PhD Trainee at the European Central Bank, Oct 2024-ongoing
Doctoral researcher at Inria and Université Côte d'Azur, Oct 2021-Sep 2024
Teaching assistant at Université Côte d'Azur, Oct 2021-Sep 2024
Machine Learning research intern at Inria, Mar 2021-Sep 2021
Machine Learning engineer intern at Alten, Sep 2020-Feb 2021
Deputy Manager of IT Departement at at JEToP, Oct 2017-Apr 2018
IT Consultant at JEToP, Oct 2016-Apr 2018
Research
You can find my code on Github and my publications on Google Scholar.
G. Lopardo, F. Precioso, D. Garreau, Attention Meets Post-hoc Interpretability: A Mathematical Perspective, ICML 2024 [paper] [code]
G. Lopardo, F. Precioso, D. Garreau, Faithful and Robust Local Interpretability for Textual Predictions [preprint] [code]
G. Lopardo, F. Precioso, D. Garreau, Understanding Post-hoc Explainers: The Case of Anchors, 54es Journées de Statistique 2023 [paper][code]
G. Lopardo, F. Precioso, D. Garreau, A Sea of Words: An In-Depth Analysis of Anchors for Text Data, AISTATS 2023 [paper][code]
G. Lopardo, D. Garreau, Comparing Feature Importance and Rule Extraction for Interpretability on Text Data, ICPR 2nd Workshop on Explainable and Ethical AI, 2022 [paper][code]
G. Lopardo, D. Garreau, F. Precioso, G. Ottosson, SMACE: A New Method for the Interpretability of Composite Decision Systems, ECML 2022 [paper][code]
Teaching
During my PhD, I also taught undergraduate courses in science and economics, as well as master's level courses within the Mathematical Engineering master program of Université Côte d'Azur.
2023-2024 (64 hours)
Mathematical Statistics (MSc, 1st year, Mathematical Engineering, 36 hours)
Statistics 2 (BSc, 2st year, Economics, 21 hours)
Probability and introduction to statistics (BSc, 2st year, 7 hours)
2022-2023 (64 hours)
Mathematical methods, classroom exercises and R laboratory (BSc, 2nd yer, 48 hours)
Introduction to Mathematics (BSc, 1st year, 16 hours)
2021-2022 (40 hours)
Mathematical Statistics (MSc, 1st year, Mathematical Engineering, 16 hours)
Fundamentals of mathematics (BSc, 1st year, 24 hours)
Contact
Email: gianluigilopardo@gmail.com
Twitter: https://twitter.com/gigilopardo
