I am a theoretical physicist by training with a strong quantitative background and extensive experience in programming, data analysis, statistics and machine learning. After my master, my keen interest in complex systems eventually brought me to the field of neuroscience.

As a Ph. D. student, mywork focused on the non-trivial processing of external information by neural systems in decision-making. I analyzed neural data from monkeys and behavior and modelled them using the framework of partially observable Markov decision processes and ideas from reinforcement learning. As a CENTURI postdoctoral fellowship I analysed behavioural data from rats using advanced machine learning and deep learning techniques. Simultaneously, I used computational models relying on deep reinforcement learning to reproduce animals behaviour and to provide a conceptual understanding of behavioral results. During my academic career I also directly supervised many students and I acquired extensive teaching experience, both in experimental physics and, more recently, in machine learning.

My main goal within the CENTURI multi-engineering platform is to provide advice and support to the neuroscientific community with signal processing and data mining. Furthermore, I will be in charge of organizing and conducting courses on data analysis (mainly in Python) for the CENTURI community, and to promote software sharing and code transfer.

Keywords:

• Signal processing and data mining
• Machine Learning: unsupervised/supervised learning, dimension reduction
• Deep Learning
• Statistics
• Python, SciPy, Sklearn, Pytorch
• Jupyter
• Matlab