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Research lines

Research Streams

MagIC's strength lies in the synergy between its Management and Data Science Research Streams, each with four distinct Research Lines.

In Management, MagIC comprises 4 Research Lines — Information Systems, Marketing, Finance and Risk Management, and Public Policy — where Data Science's analytical proficiency plays a pivotal role. Through advanced analytics techniques, Data Science enhances available data sources, unveils concealed patterns, and fosters a deeper understanding of critical aspects such as consumer behaviour, team dynamics, and operational efficiency.

Concurrently, Management research provides indispensable context and problem-framing, facilitating the creation of targeted data-driven solutions leveraging research in the Data Science Research stream, specifically in Evolutionary Computation, Deep Learning, Synthetic Data Generation, and Geoinformatics Research Lines.

Five thematic lines intersect and connect all Research Lines to address societal challenges: Health, Smart Cities, Sustainability, Tourism and Hospitality, and Education.

Research Stream Management

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    Information Systems

    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

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    Marketing

    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

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    Finance and Risk Management

    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

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    Public Policy

    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

Research Stream Data Science

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    Evolutionary Computation

    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

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    Deep Learning

    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

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    Synthetic Data Generation

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

     

    Coordinator: Fernando Bação

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    Geoinformatics

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

     

    Coordinator: Marco Painho