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Computer Vision
Computer Vision
I am broadly passionate about applications of machine learning to computer vision problems. My main research interests at the moment are continual learning, domain adaptation, domain generalization and model robustness.
At NLE, I lead a project whose main goal is providing computer vision models for robotics systems with continual learning/adaptation capabilities.

I joined NLE at the beginning of 2020, as part of the CV team.


Before that, I was a Ph.D. student first (2015-2018) and a postdoc then (2018-2019) at Istituto Italiano di Tecnologia. During my Ph.D., I also spent six months at Stanford University.

  • December 2021: our new paper "Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey" is available on arXiv.
  • October 2021: we have an open position for an internship about class-incremental learning.
  • June 2021: new blog post on our CVPR 2021 paper.
  • March 2021: our paper "Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning" has been accepted to CVPR 2021 (Oral). More info in our project page.
  • July 2020: new blog post on continual learning, "The Short Memory of Artificial Neural Networks".


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