I'm Valentina, a doctoral researcher in Machine Learning.
My research interests cover metric learning, weakly-labeled data, local learning and decentralized learning, adversarial robustness and ML applied to climate science.
My PhD focused on kernel learning with theoretical guarantees on performance. In particular, studying new data-dependent ways for approximating the Gram matrix and for learning the kernel function as well, through the selection of representing inputs.
In 2019, I took part to FDL research accelerator, and start working on ML applied to climate science, in particular for performing cloud classification at global scale. The collaboration with Matt Kusner, Duncan Watson-Parris and Fabrizio Falasca resulted in the release of CUMULO dataset.
In 2017, I worked as an intern at IBM Research, Dublin, in Mathieu Sinn's team, on studying and building Deep Learning architectures robust to adversarial examples. The library developped for this research work served as codebase of the initial release of Adversarial Robustness Toolbox.
Previously, I graduated in 2015 from INSA de Lyon, France, in Computer Science.