Emilia Siviero

Emilia Siviero is a Post-doctoral Researcher at Università Ca' Foscari, Venezia in the Department of Environmental Sciences, Informatics and Statistics (DAIS), working on Spatial Statistics and Extreme Value Theory under the supervision of Ilaria Prosdocimi and Carlo Gaetan. Previously, she earned her PhD at Télécom Paris with a thesis titled Statistical Learning for Spatial Data: Theory and Practice, focusing on spatial statistics and spatio-temporal Hawkes processes.

Her research spans theoretical and applied work in

• Spatial Statistics,
• Nonparametric Covariance Estimation,
• Spatio-temporal Hawkes Processes, and
• Extreme Value Theory.

Recent publications address parametric inference for Hawkes processes, Kriging from a machine learning perspective, and stochastic algorithms in optimal transport. She has presented at international conferences such as COMPSTAT and CAp, and organized events like the Chaire DSAIDIS day.

Her teaching experience includes courses on Statistical Learning, Machine Learning, and Probability for Télécom Paris and Mines ParisTech students. She is actively involved in computational statistics and climate data analysis projects, with software tools available on GitHub.