I'm Valentina, Sr Research Scientist at ServiceNow Research, Adjunct Professor at Laval University, Quebec, and member of the ELLIS Society. My research focuses on building ML models that are inherently interpretable via latent discrete structures. My works span discrete optimization, structure learning, voting systems and time-series forecasting.

Previously, I was a post-doctoral researcher at INRIA and University College London, in the context of the INRIA-London Programme, working in Benjamin Guedj's team on PAC-Bayesian learning, and a mentor/researcher at FDL research accelerator, working on Deep Learning applied to climate science and Earth Observational data, in particular for performing cloud classification and weather forecasting at global scale, and ML on-board of satellites. An outcome of these projects was the release of CUMULO dataset.

I received my PhD in Computer Science from Jean Monnet University (Saint-Etienne, France), in the Data Intelligence team of Hubert Curien Lab., advised by Marc Sebban and Rémi Emonet. 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 2017, I worked as a research intern at IBM Research, Dublin, in Mathieu Sinn's team, on studying and building Deep Learning architectures robust to adversarial examples. The library developed for this research work served as codebase of the initial release of Adversarial Robustness Toolbox.

I graduated in 2015 from INSA de Lyon, France, in Computer Science. I am originally from Italy and I am now based in Montreal, Canada.