Research

At Teesside University I am part of the Interpretable and Beneficial Artificial Intelligence research group, and my research activity aims at bridging AI, Game Theory, and Complex Networks for making Inferences, and getting interpretable and explainable decisions.

Below I illustrate the main motivations, research questions and concepts, methods and data characterising part of my research activity.

Motivations

The target has been to gain a better understanding of the complex dynamics of human behaviours, such as social or dietary behaviours, social contagion processes, epidemic spreading, etc., unveiling the impact of hidden human-related factors.

Questions

Here some of the research questions: if the first question is addressed by game theory, the second one is related to complex networks and network science, while the last one introduces bio-inspired and human-related factors incorporated in our modelling approaches.

Concepts

These are the main concepts embodied in my methodologies and based on which the main statistical estimators have been defined.

Methods

Here I summarise the methods used in my research activity and how these are linked with each other.

Data

Below the different types of multi-scale data used: from social networks data to user’s google searches and trends of some words, to socio-economic, metabolic or other healthcare data provided by the WHO on specific diseases or virus, or data from MCS services (e.g., Waze).