Hello! I'm Federica Granese, PhD student in Computer Science at École Polytechnique (Paris) and Sapienza University (Rome).
I'm supervised by Catuscia Palamidessi & Pablo Piantanida, COMETE Team, Inria-Saclay and Daniele Gorla, Sapienza.
My research is mainly focused on Safety and Security in Machine Learning.
You can find here my CV.
Talks, events and more
| When | What |
|---|---|
| Oct. 2022 - Feb. 2023 | I am thrilled to announce that I have begun a Research Internship at École de technologie supérieure (ÉTS) in Montreal, Quebec.
Error detection in image segmentation tasks..
Supervised by Prof. José Dolz. |
| 2022 | Our paper MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detector, has been accepted at ECML-PKDD! Check out the presentation. |
| 2021 | Our paper DOCTOR: A Simple Method for Detecting Misclassification Errors, has been accepted at Neurips2021 as Spotlight! Special thanks to Pablo and Marco for their work! Check out the presentation. |
A selection of recent publications
Papers
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detector,
Granese, F., Picot, M., Romanelli, M., Messina, F., Piantanida, P.
ECML-PKDD 2022.
DOCTOR: A Simple Method for Detecting Misclassification Errors,
Granese, F., Romanelli, M., Gorla, D., Palamidessi, C., Piantanida, P.
NeurIPS 2021 (Spotlight).
Enhanced models for privacy and utility in continuous-time diffusion networks,
Granese, F., Gorla, D. & Palamidessi, C. Int. J. Inf. Secur. (2021).
[Text]
Enhanced models for privacy and utility in continuous-time diffusion networks,
Gorla, D., Granese, F., Palamidessi, C. Theoretical Aspects of Computing – ICTAC 2019 pp 313-331.
Bachelor thesis
Polynomially Recognising Graphs Where Saturating Flows Are Always Maximum, supervised by Daniele Gorla.
You can find me here:
Bâtiment Alan Turing, Inria Saclay - Campus de l'École Polytechnique
1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
Office, 2068
GET IN TOUCH
federica.granese@inria.fr
granese@di.uniroma1.it