Bio

I’m a Research Scientist at Amazon, working on Anomaly Detection for Computer Vision applications. I have a PhD in Computer Vision from Politecnico di Torino where I have been part of the Vandal lab. I’m passionate about new technologies as I’m convinced they can improve our life. Still, I firmly believe these improvements should benefit all: knowledge should be open, personal data should stay private.

News

I have finally graduated as a PhD by defending my Thesis titled Addressing Distributional Shift challenges in Computer Vision for Real-World Applications.

Our paper Foundation Models and Fine-Tuning: A Benchmark for Out of Distribution Detection has been published as a Journal article on IEEE Access, becoming my last paper published as PhD student.

I have joined Amazon as a full-time Research Scientist!

I’m happy to have joined Amazon Luxembourg as a Research Scientist Intern working on anomaly detection!

Our paper Large Class Separation is not what you need for Relational Reasoning-based OOD Detection has been accepted for publication at ICIAP 2023

I am proud to have been named one of CVPR 2023 outstanding reviewers

Our paper Towards Open Set 3D Learning: Benchmarking and Understanding Semantic Novelty Detection on Pointclouds has been accepted for publication at NeurIPS Datasets and Benchmark 2022!

Our Journal article Self-supervision & meta-learning for one-shot unsupervised cross-domain detection published in the Computer Vision and Image Understanding (CVIU) Journal is now available online.

From 7 to 9 October I’m going to attend Maker Faire Rome (2022) Edition as part of the Vandal Lab delegation

From 20 to 29 October I will be in Kyoto (Japan) to attend IROS 2022 and present our paper Contrastive Learning for Cross-Domain Open World Recognition

Our paper Semantic Novelty Detection via Relational Reasoning has been accepted for publication at ECCV 2022!

Our paper Contrastive Learning for Cross-Domain Open World Recognition has been accepted for publication at IROS 2022!