Health

We develop computational models and analytical approaches for clinical datasets, incorporating explainability mechanisms tailored for medical applications. Additionally, we advance technological interfaces and medical devices—including neurostimulation systems, brain-computer interfaces, and computer vision technologies—to improve patient diagnosis, treatment, and therapeutic interventions.

Research areas

Computational Biology

Our research focuses on developing statistical and machine learning models to analyse, harmonize, and integrate diverse types of omics data — including genomics, transcriptomics, proteomics, and metabolomics — as well as pharmacology and CRISPR-based screens. We work on generative AI and multi-modal models, along with sparse regularization methods for high-dimensional omics data and joint models for longitudinal data analysis, with a strong emphasis on clinically cancer applications, including multi-omics and electronic health records integration and the development of predictive models for diagnosis and overall survival analysis.

Key researchers: Emanuel Gonçalves, Susana Vinga, Arlindo Oliveira

Computer Vision & Medical Imaging

Our research focuses on the development of advanced algorithms for image and signal processing, with applications in cancer, cardiology, and neurology. We work with a variety of biomedical data types, including ECGs, whole-slide histopathology images, angiographies, and CT scans, aiming to improve diagnostic accuracy, automate analysis, and support clinical decision-making through computational innovation. We also work in biologically inspired architectures for vision.

Key researchers: Arlindo Oliveira, Catarina Barata

Data-driven public health policy

We develop algorithms to model the spatiotemporal spread of pandemics, with particular emphasis on deep learning approaches that integrate heterogeneous data sources. By combining information from epidemiological, environmental, mobility, and social datasets, we aim to create robust models capable of capturing complex transmission dynamics and supporting data-driven public health responses.

Key researchers: Arlindo Oliveira

Dynamical Systems and Biomedical Engineering

Our work in this domain encompasses research on wearable and invisible devices for physiological data acquisition, coupled with biosignal processing and artificial intelligence for knowledge extraction from multimodal health data, with applications to clinical support, wellbeing and lifestyle, and human-computer interaction. We also work on developing dynamical systems models and control algorithms for brain activity that enable accurate state estimation and closed-loop neurostimulation strategies to mitigate conditions like epilepsy.

Key researchers: Hugo Silva, Sergio Pequito

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