Intelligent Systems

The Intelligent Systems (IS) group is formed by a dynamic, multidisciplinary, and innovative research team specialised in developing intelligent ICT solutions that leverage AI, computer vision, time series, and natural language processing to address real-world challenges. The team strives to push the boundaries of AI and related fields, collaborating closely with industry partners, academia, and other research institutes to foster innovation and create practical applications and solutions that positively impact society, industries, and individuals. With a main focus on health and production industry, we support our clients throughout the entire research and development process. From defining a suitable approach and roadmap to address their specific business problems to collecting, analysing, refining, or even synthetically generating data, we ensure a solid foundation for our AI-driven solutions. Moreover, we are committed to providing reasoned, ethically responsible, and trustworthy AI decisions, recognising the importance of transparency in implementing AI technologies. Finally, we support the seamless integration, monitoring, and maintenance of intelligent solutions, enabling our clients to harness the full potential of AI.


Data-driven intelligence

How can we extract meaningful insights from high-dimensional, unstructured data? This sub-field aims to address this question by leveraging our strong background in classical time series and image processing within machine learning. We focus on char-acterizing unstructured data, identifying patterns, predicting events or longitudinal trends in time series, and extracting relevant image features, including objects, sub-structures, patterns, and textures. Furthermore, we combine different modalities to obtain a broader understanding of the phenomena under study. Our research also encompasses anomalies, out-of-distribution detection, and domain-driven data quality evaluation in real-world and synthetic scenarios. We articulate with HCD in mapping and utilizing domain knowledge and with CT in data engineering developments.


Learning in low-data scenarios

How can we address the real-world problem of low data availability? This sub-area focuses on developing advanced learning techniques that can operate effectively in low-data scenarios. We explore few-shot learning, transfer learning, and fine-tuning of large models to maximize the utility of available data. Additionally, we investigate generative models for data synthesis, creating new data that can help improve model performance. Our research also extends to distributed and federated learning, which allows us to leverage data from multiple sources while preserving privacy and reducing data movement.


Trustworthy and Operational AI

How can we ensure that AI models are accurate, reliable, interpretable, and easily deployable in real-world applications? This research line is dedicated to developing robust AI pipelines that can withstand data perturbations and distribution shifts. We focus on privacy-preservation techniques to protect sensitive information, and uncertainty quantification to provide a measure of confidence in our models' predictions. Our work, in tandem with HCD, also includes human-centred and explainable AI, by meaning-making and ensuring that our models’ decisions can be understood by humans. Lastly, together with CT, we aim to develop models that can be deployed on the edge, bringing the power of AI closer to where the data is generated and used.

Further information


Competence Articles


TSFEL: Time Series Feature Extraction Library


Relevant Services


AI Strategy Workshop

Data Scientist Specialized in Data Analytics

Deep Learning

Machine Learning


Relevant Publications


Folgado, D.; Barandas, M.; Famiglini, L.; Santos, R.; Cabitza, F.; Gamboa, H. (2023). Explainability Meets Uncertainty Quantification: Insights from Feature-based Model Fusion on Multimodal Time Series, Information Fusion, p.48. More info

Madeira, P., Carreiro, A., Gaudio, A., Rosado, L., Soares, F., & Smailagic, A. (2023). ZEBRA: Explaining Rare Cases Through Outlying Interpretable Concepts. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 3781–3787. More info

Oliper, D., Rolla, V., Souper. T., (2023). Predictive Maintenance Approaches with Free-text Labels: A Case Study in the Oil Industry, AI4BPM Workshop at the Association for the Advancement of Artificial Intelligence (AAAI) conference. More info

Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., Cabitza, F., & Gamboa, H. (2021). Interpretable heartbeat classification using local model-agnostic explanations on ECGs. Computers in Biology and Medicine, 133, 104393. More info

Barandas, M., Folgado, D., Fernandes, L. P., Santos, S., Abreu, M., Bota, P. J., Liu, H., Schultz, T., & Gamboa, H. (2020). TSFEL: Time Series Feature Extraction Library. SoftwareX, 11, 100456. More info

Silva, J., Sousa, I., & Cardoso, J.S. (2020). Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls. In Journal of Biomedical And Health Informatics, 24(1), 50-56. DOI: 10.1109/JBHI.2019.2951230. More info

Martins, J., Cardoso, J.S., & Soares, F. (2020). Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices. In Computer Methods and Programs in Biomedicine, 192. DOI: 10.1016/j.cmpb.2020.105341. More info