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. 

Coordinator: Tiago Oliveira

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. 

Coordinator: Diego Costa Pinto

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. 

Coordinator: Jorge Bravo

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. 

Coordinator: Pedro Simões Coelho

Our research advances Genetic Programming (GP), improving its semantic awareness, introducing regularization methods and several hybrid systems and developing vectorial-based GP.   

Coordinator: Leonardo Vanneschi

Our research crafts landscape analysis tools for topology and parameter estimation, developed neuroevolutionary systems and applied deep models to several real-world tasks.

Coordinator: Mauro Castelli

Our research designs algorithms to improve state-of-the-art oversampling techniques and enhance model robustness and accuracy.  

Coordinator: Fernando Bação

Our research analyzes urban perception, models geospatial phenomena, and monitors ecosystem services through land cover and remote sensing. 

Coordinator: Marco Painho

Our data-driven approaches improve diagnostic accuracy, personalize treatment strategies, and enhance patient care across medical domains. We develop innovative solutions for managing chronic diseases, predicting mortality, and contributing to mobile health, electronic health portals, and video consultations. 

Our research analyses complex combinations of large datasets in areas of public interest, namely Micromobility, Parking, Pollution, and Emergency. We have developed a pilot-scale digital smart city platform for urban modelling and planning, and an IoT-based system to facilitate Pay-As-You-Throw and recycling reward schemes. 

Our research deepens the understanding of sustainable technologies and energy communities, developing strategies and toolkits for emerging ST and energy communities and solutions for improving the quality of life in urban environments. We promote energy citizenship, allowing citizens to take control of their path to sustainability.

Our research uses Data Science to analyse trends in hospitality and tourism, exploring social influence, technology within sharing economy platforms, and customer acceptance of AI-based services. We address the challenges and opportunities in an increasingly digital landscape.

Our research sheds light on the most important antecedents and outcomes of e-learning technologies, and identifies the main drivers and barriers for academic achievement in high schools. We develop innovative data-driven approaches, apply machine learning techniques for timely prediction of student performance, and conduct exploratory analyses of higher education census data. We also contribute to the area of education by finding the main determinants of e-learning systems success.

Our research analyses complex combinations of large datasets in areas of public interest, namely Micromobility, Parking, Pollution, and Emergency. We have developed a pilot-scale digital smart city platform for urban modelling and planning, and an IoT-based system to facilitate Pay-As-You-Throw and recycling reward schemes.