Our research explores sustainable technologies, digital transformation strategies, and technology adoption/success drivers. We use Data Science to analyze large datasets and uncover insights into innovation diffusion, technology adoption, decision support, and organizational impacts.
Our research implements data-driven marketing strategies and applications/tools, focusing on consumer behaviour, digital/social media marketing, social influence, human-technology interactions, and social marketing.
Our research applies Data Science to develop advanced financial models and identify factors in green banking. We also contribute to financial economics and risk management using machine/deep-learning stochastic mortality models and Big Data.
Our research develops Data Science-based methods for policy evaluation and building public administration capacity, including impact assessment, policy evaluation, and producing evidence to support public policies.
Our research advances Genetic Programming (GP), improving its semantic awareness, introducing regularization methods and several hybrid systems and developing vectorial-based GP.
Our research crafts landscape analysis tools for topology and parameter estimation, developed neuroevolutionary systems and applied deep models to several real-world tasks.
Our research designs algorithms to improve state-of-the-art oversampling techniques and enhance model robustness and accuracy.
Our research analyzes urban perception, models geospatial phenomena, and monitors ecosystem services through land cover and remote sensing.