Projects
Time Series Analysis
Methods to analyze multivariate signals
- Forecasting methods for complex time series
- Higher-order inference in temporal data
- Arrow of time in time-varying signals
Computational Neuroscience
Development of computational approaches to understand neural systems, at micro- and macro-scale level.
- Higher-order topological approaches of brain connectivity
- Structure-function approaches to analyze fMRI data
- Machine learning approaches to neuroimaging data in healthy and clinical population
Multilayer Networks
Investigation of multilayer network structures and their applications in understanding complex interconnected systems.
- Complexity and reducibility of multiplex networks
- Models and measures for multiplex networks
- Strategies of optimal percolation for multiplex networks
Project CETI
Project CETI (Cetacean Translation Initiative) is an ambitious interdisciplinary endeavor aiming to decode the communication of sperm whales. By leveraging advanced machine learning, data analysis, and linguistic theory, we seek to interpret the complex vocalizations of these intelligent marine mammals. Understanding their communication patterns offers insights into their social structures and into language and cognition across species.
- Foraging strategies from acoustic recordings and 3D dive trajectories (bioRxiv 2026)
- Collaborative sperm whale birth and shifts in coda vocal styles (Scientific Reports 2026)
- More on the NPLab CETI project page
Computational Gastronomy
Network approaches to uncover the principles behind how ingredients are combined in cuisines around the world.
- Networks of ingredient combinations as culinary fingerprints of world cuisines (npj Science of Food)
- Maximum spanning trees as simplified backbones of cuisines
- Machine learning to identify cuisines from recipes, and clustering of cuisines into geo-cultural groups
- Chef Network Platform