# Teaching Assistants

This course would have not been possible without the help of two great **teaching assistants**: [Antonio Carta](http://pages.di.unipi.it/carta/) and [Andrea Cossu](https://andreacossu.github.io/)! They helped us significantly improve the quality of the material and offered their technical/didactic support along the entirety of the course!

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*Please refer to them and the* [*course instructor*](/about-us/your-instructor.md) *for any issue you may have.*&#x20;
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<div align="center"><img src="/files/YQ3no60njZkgQjLmLFkg" alt="Andrea Cossu"></div>

[**Andrea Cossu**](https://andreacossu.github.io/) *is a PhD Student in Data Science, under the supervision of* [*Davide Bacciu*](http://pages.di.unipi.it/bacciu/)*,* [*Vincenzo Lomonaco*](https://www.vincenzolomonaco.com/) *and* [*Anna Monreale*](http://pages.di.unipi.it/amonreale/)*. His research focuses on Continual Learning, with applications to Recurrent Neural Networks models and sequential data processing. He is a member of the* [*Pervasive AI Lab*](http://pai.di.unipi.it/) *and of the* [*Computational Intelligence and Machine Learning (CIML)*](https://ciml.di.unipi.it/) *group at University of Pisa. He is a Board Member and Treasurer of* [*ContinualAI*](https://www.continualai.org/)*.*\
*He is also the Principal Maintainer of* [*ContinualAI wiki*](https://wiki.continualai.org/) *and one of the main maintainers of* [*Avalanche*](https://avalanche.continualai.org/)*, an End-to-End library for Continual Learning based on* [*PyTorch*](https://pytorch.org/)*.*

![Antonio Carta](/files/JLAUoCf1xICqVHqiDdNj)

[**Antonio Carta**](http://pages.di.unipi.it/carta/) *is a Post-Doc in the Department of Computer Science at the University of Pisa, under the supervision of Davide Bacciu. He is also a member of the Computational intelligence and Machine Learning group (* [*CIML*](https://ciml.di.unipi.it/)*) and Pervasive AI Lab (* [*PAI*](http://pai.di.unipi.it/)*) at the University of Pisa, and a member of ContinualAI. His research is focused on continual learning methods applied to deep learning models and recurrent neural networks.*


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