Hi there!đź‘‹
About🤌Â
I'm Gianluigi, an applied mathematician with a strong interest in machine learning and computational linguistics.Â
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 UniversitĂ© CĂ´te d'Azur. My PhD thesis centered on the Foundations of Machine Learning interpretability, supervised by Damien Garreau and FrĂ©dĂ©ric Precioso. Previously, I got an MSc in Mathematical Engineering and a BSc in Applied Mathematics, both from Politecnico di Torino.Â
News
14th October 2024: I successfully defended my PhD thesis on the Foundations of Machine Learning interpretability! 🥳Â
1st October 2024: I started working for the International Policy Analysis Division of the European Central Bank 🇪🇺Â
Past
July 21-27: In Vienna for ICML 2024
June 2-15: visiting the Julius-Maximilians-Universität WĂĽrzburgÂ
May 2024: Our paper Attention Meets Post-hoc Interpretability: A Mathematical Perspective got accepted to ICML 2024! 🥳🥳🥳Â
February 2024: new preprint! We investigate the relation between attention-based and post-hoc explanations Â
November 30, December 1: I've been at the 2nd Nice Workshop on Interpretability
November 24: talk to the Maasai seminarÂ
November: new preprintÂ
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 AISTATSÂ Â
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 SMACEÂ
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 WeekÂ
November 19, 2021: talk at the SophIA SummitÂ
November 2021: new preprint available: we propose a new method for the explainability of composite AI systems
October 1, 2021: I started my PhD
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
Education
Ph.D. in Applied Mathematics, 2024
Inria & UniversitĂ© CĂ´te d'AzurÂM.Sc. in Mathematical Engineering, 2021
Politecnico di TorinoB.Sc. in Applied Mathematics, 2019
Politecnico di Torino
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 UniversitĂ© CĂ´te d'Azur.Â
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
