Home page

Research - My research interests are at the intersection between the areas of Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems. I currently work on dynamical neural models, reservoir computing, deep learning for graphs, stable neural architectures, and neuromorphic computing.

A short version of my CV is available here.

Students' Supervision - Drop me a line at my email without hesitation.
I am happy to supervise BSc, MSc and PhD students on topics related to

• Neuromorphic Computing for AI: using the physical laws and substrates (e.g., photonics, memristors, etc.) to perform neural computation.

• Reservoir Computing: randomized recurrent neural networks in which only a small portion of the neural connections need to be trained.

• Stable neural architectures: neural networks can be seen as dynamical systems as they unfold in time (e.g., RNNs) or in space (e.g., deep feed-forward architectures); here we design architectures that behave well from the information processing perspective so that gradients and forward computation do not vanish by design.

• Beyond backpropagation learning: learning algorithms that are biologically plausible and efficient.