Main Research themes:

🎓 Theoretical Physicist graduated from the University of Milan Bicocca. My academic education is based on a strong background in mathematics and statistics, with particular attention to numerical simulations. Moreover, I specialized in Machine Learning and Neural Network algorithms, with application to Big Data analysis.

🧠 I’m currently working on new algorithm for Brain Computer Interfaces (BCI) classification with particular focus on Motor Imagery using electroencephalography (EEG). I’m mainly focus on extending the concept of Riemannian classification algorithm in combination with concept of non linear dynamical theory. Recently I’m focusing some attention on the carbon footprint of the deep learning algorithm and on their explainability applied to Motor Imagery classification problem.

💻 My skills cover: Big Data, Data Analyst, Mathematical and Computational Modeling, Strong Background in Mathematics and Statistics, Scientific Method, Machine Learning, Neural Network, Deep Neural Network.

🚀Python Package mostly used: MNE Python, MOABB, Keras, Tensorflow, Pytorch, Scikit-Learn, CodeCarbon, Weight&Bias.

🌍I’m currently one of the main contributor of Mother of all BCI Benchmarks (MOABB), an open library in Python for compare different BCI algorithm in a fair way.

👨🏼‍🏫I am also teaching a master courses at Université Côte d’Azur :

• Introduction to Machine Learning (with E. Sarti).