The aim of ACG is the analysis and development of computational models and algorithms relying on data analytics and soft computing techniques for solving complex problems. The group’s activities are structured in four main research streams: machine learning and pattern recognition areas, algorithms and optimization, personalized health care, and smart heterogeneous systems.

The group leads and participates in several national (FCT, FCG, QREN, P2020) and international projects (FP7, H2020, COST, EUREKA, INTERREG) and organized several international workshops and conferences

> In machine learning and pattern recognition, research topics include dynamic models for ensemble learning in data streams with drifts (e.g. social networks), graph mining algorithms (e.g. biological networks, financial applications). Other research topics include probabilistic models, Gaussian Processes with active learning (e.g. transport systems) and multimodal learning with convolutional neural networks and transfer learning (e.g. image, text, video and audio).

> Research on algorithms and optimization focuses on the development, implementation and evaluation of exact and heuristic methods for numerical and combinatorial optimization involving multiple objective functions and incomplete preference information provided by a Decision Maker.

> In healthcare, the group has developed and implemented innovative solutions, mainly supported by computational intelligence methodologies, for personalized non-invasive remote monitoring applications (pHealth), able to support the management of diseases and early diagnosis of critical events. Applications are in preventive medicine and in chronic disease management, namely cardiovascular and cardiopulmonary diseases, sleep and neurological disturbances (epilepsy).

> In the context of smart systems, the focus includes resilient enhancement in networked supervisory systems and optimization over heterogeneous networks (fuzzy system, multi-agent-based algorithms, data streaming analysis, and GIS based management of river and urban floods). Innovative solutions for remote and virtual laboratories have been developed to support research and training activities in collaborative environments for various targeted areas (platform development, testbed-based applications and algorithms assessment).