| Name | Gian Marco Paldino |
| Association | PHD |
| Role | PhD Student |
| Resume | Gian Marco Paldino, M.Sc.² in Computer Science and Engineering from Université Libre de Bruxelles and from Politecnico di Milano, graduated respectively “Grande Distinction” (ULB) and 110L/110 “with honors” (POLIMI) with the thesis “Incremental Learning for Credit Card Fraud Detection”, focusing on industrial fraud detection in non-stationary environments, concept drift adaptation and transfer learning. Currently enrolled in a PhD program, his research interests are Credit Card Fraud Detection, Automated Machine Learning (AutoML), Time Series Forecasting, Digital Twin, Causality. |
| General info | Teaching assistant for the following courses (2020/2021): INFO-F-101: Programmation |
| Research | Incremental Learning |
| Selected publications | Paldino GM, De Caro F, De Stefani J, Vaccaro A, Villacci D, Bontempi G. A digital twin approach for improving estimation accuracy in dynamic thermal rating of transmission lines. Energies. 2022 Mar 19;15(6):2254. Paldino GM, De Stefani J, De Caro F, Bontempi G. Does automl outperform naive forecasting?. Engineering proceedings. 2021 Jul 5;5(1):36. Paldino GM, Lebichot B, Le Borgne YA, Siblini W, Oblé F, Boracchi G, Bontempi G. The role of diversity and ensemble learning in credit card fraud detection. Advances in Data Analysis and Classification. 2022 Sep 28:1-25. Lebichot B, Paldino GM, Bontempi G, Siblini W, He-Guelton L, Oblé F. Incremental learning strategies for credit cards fraud detection. In2020 IEEE 7th international conference on data science and advanced analytics (DSAA) 2020 Oct 6 (pp. 785-786). IEEE. Lebichot B, Paldino GM, Siblini W, He-Guelton L, Oblé F, Bontempi G. Incremental learning strategies for credit cards fraud detection. International Journal of Data Science and Analytics. 2021 Aug;12(2):165-74. Coelho LB, Torres D, Bernal M, Paldino GM, Bontempi G, Ustarroz J. Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution. Corrosion Science. 2023 Mar 13:111104. Coelho LB, Torres D, Bernal M, Paldino G, Bontempi G, Ustarroz J. Data-driven analysis of the local current distributions of 316L stainless steel corrosion in NaCl solution. |
