Assistant Professor and Head of the Games Study Program, Center for Computer Games Research.
Areas of Competence
Machine Learning, Artificial Intelligence, Data Science, Analytics, Player Modelling, Generative Adversarial Networks, Imitation Learning
Current Research Interests
Live game operations, Imitation Learning, Player Modelling, Churn Prediction and Customer Lifetime Value Prediction
Paolo is a researcher and scholar in the field for data science and artificial intelligence. He is an expert in machine learning for user behaviour prediction, and has experience in large scale data ingestion, data processing processing, feature extraction and modelling at both prototyping and production stages.
His research focus lies primarily around the problem of making machines understand human behaviour. He started researching this topic during his PhD studies by investigating how to make games react to players and provide continuously the best view over the game actions. Since then, Paolo's research has further expanded to other applications of user modelling, attempting to create predictive models of user behaviour either from high density data, such as EEG and other physiology signals in lab contexts, or from large user datasets in the context of performance marketing automation.