Research

For an updated list of publications, please check my Google Scholar profile.

I work on Computer Vision and Multimodal Deep Learning, with applications in domains ranging from biomedical to aerial imaging. Feel free to get in touch if you’re interested in any of these topics.

Projects

Multimodal Failure Prediction for Assistive Human-Robot Interaction

Development of a deep learning-based module to predict failures in assistive human-robot interaction using multimodal data. Leveraging vision, wearable, and proprioceptive signals, the system anticipates anomalies during task execution, contributing to robust and user-aware augmentation in Task 9.1 of HARIA project.

Funding Entity Horizon Europe. Site: http://haria-project.eu/
Duration 2024 - present
Team João Bimbo (PI), NCG

Deep Learning-based Digital Twin of Manufacturing Systems

The project aims to develop a real-time, deep learning-based digital twin system for manufacturing environments using computer vision. It focuses on combining real and synthetic visual data to train robust object detection models that can monitor and optimize production processes—including human activity—within a flexible and automated industrial setup.

Funding Entity UNITE!. Duration 2024 - present
Team NCG; with University Grenoble Alpes: Pierre David, Leah Rifi, Nessrine Farhat

Deep Multimodal Learning for Breast Cancer Detection

We are developing self-supervised methods for imaging tasks related breast cancer, using 2D and 3D data, and several modalities such as synthetic data, tomosynthesis, mammography, and ultrasound.

Funding Entity FCT (EXPL/CCICOM/ 0656/2021). Duration 2022-2023; 2024-present
Team NCG and Nuno Matela

THOR - Computer Assisted Thoracic Assessment using POCUS

Working mainly on Point-of-Care Ultrasound assisted navigation and diagnosis.

Funding Entity FCT. Duration 2021-2024; 2024 - present
Team Hugo Ferreira and NCG

AIpALS - Advanced LearnIng Models using Patient Profiles and Disease Progression Patterns for Prognostic Prediction in ALS

Studying disease progression using recurrent neural networks.

Funding Entity FCT. Site: https://www.lasige.pt/project/aipals/
Duration 2021-2024
Team Sara Madeira (PI)

PAIR-LUNG – Patient-derived lung cancer organoids for recreating tumor spread through AIR spaces phenomena.

Developing deep learning methods using confocal images and histological sections.

Funding Entity FCT. Duration 2023-2025
Team with Centre for Toxicogenomics and Human Health, Nova Medical School: Bárbara Mendes (PI)

COST Distributed Knowledge Graphs

Our Action is centred around the topic of Distributed Knowledge Graphs, i.e., Knowledge Graphs that are published in a decentralised fashion, thus forming a distributed system.

Funding Entity nan. Site: https://cost-dkg.eu/
Duration nan
Team nan


Supervisions

Current

PhD Students
  1. Iordanis Antypas, AI-powered Failure Management Framework for Human-Robot Sensorimotor Augmentation
    FCUL. Co-advisors: João Bimbo.
  2. Nessrine Farhat, Deep Learning-based Digital Twin of Manufacturing Systems
    FCUL; Université Grenoble Alpes. Co-advisors: Pierre David; Leah Rifi.
  3. João Mendes, Breast Cancer Risk Prediction: Combining multiple imaging modalities in Convolutional Neural Networks
    FCUL. Co-advisors: Nuno Matela.
  4. Duarte Saraiva, Development of an AI-based assisted navigation and diagnosis solution for pulmonary point-of-care ultrasound
    FCUL. Co-advisors: Hugo Ferreira.
  5. Jessica Catarino, Using Breast Cancer Risk-based Models with Reinforcement Learning for Early Detection
    FCUL; Champalimaud Foundation. Co-advisors: Sara Silva; João Santinha;.
  6. Hugo Figueiras, Multimodal Foundation Model for Breast Cancer
    FCUL. Co-advisors: Nuno Matela.
  7. José Domingues, Vision-Language Deep Learning for Explainable Medical Imaging Diagnosis
    FCUL. Co-advisors: Nuno Matela.
  8. Beatriz Ferreira, Wearable Device Based Multi-Symptom Assessment for Parkinson's Disease Diagnosis and Monitoring
    FCUL; Dynasys. Co-advisors: Virginie Felizardo.
  9. Lisana Daniel, Data, algorithms, and platforms for management of sleep disorders at home
    FCUL; Dynasys. Co-advisors: Virginie Felizardo.

MSc Students
  1. Deep Learning for Acoustic Artefact Detection and Compensation in Echocardiography, Hugo Vieira. KU Leuven. Co-advisors: Somayeh Akbarisaghezchi.
  2. Physics-Guided Deep Learning for Sparse Data-Driven Brain Shift Registration, Tiago Assis. University of Cambridge; Inria Paris-Saclay . Co-advisors: Inês Prata Machado; Reuben Dorent.
  3. Multimodal perception for fault tolerant human-robot interaction, Guilherme Ribeiro. FCUL. Co-advisors: João Bimbo.
  4. Deep Learning for Plant Disease Detection, Eduardo Carneiro. FCUL; with Generosa.
  5. Automation and User Experience Enhancement - Generosa Plant Coach, José Ricardo Ribeiro. FCUL; with Generosa.
  6. Leveraging Large Language Models for Document Classification, Romulo Nogueira. FCUL; with Deloitte. Co-advisors: Hugo Silva.
  7. Visão Computacional para Soluções de Segurança Adicional em Máquinas de Autoatendimento, Vasco Maria. FCUL; with Innovation Makers. Co-advisors: Dino Coutinho.
  8. Sequential recommender systems for chemical data, Cláudia Afonso. FCUL. Co-advisors: Márcia Barros.
  9. Benchmarking Contrastive Self-Supervised Learning for Multimodal Medical Imaging, Martim Dourado da Silva. FCUL.
  10. Unsupervised Learning and Visualization of Astronomical Phenomena Using Self-Organizing Maps, Martim Costa. Instituto de Astrofísica e Ciências do Espaço. Co-advisors: Israel Matute.

Past

MSc Students
  1. Deep Learning with scarce resources for histopathology image analysis, Rodrigo da Silva e Santos. FCUL. 2025. document.
  2. Development and optimization of AI algorithms for the categorization of small texts from social media data related to disasters, Filipe Pedro Félix de Sousa. FCUL; with INOV-INESC. 2024. document. Co-advisors: Joana Rosa.
  3. Machine Learning Algorithm for the prediction of the direction of growth of Macular Neovascularization, João Pedro Candeias da Silva Santos. FCUL. 2024. document. Co-advisors: Nuno Matela.
  4. Using supervised machine learning to quantify cleaning behaviour, Raúl Afonso Castelhano de Oliveira. FCUL. 2024. document. Co-advisors: José Ricardo Paula.
  5. Vineyards monitoring using convolutional neural networks and multispectral images, Rodrigo Manso Teixeira Basílio Ferreira. FCUL. 2024. document.
  6. Ultrasound-Guided Lumbar Puncture with AI-Based Image Labeling for Intracranial Pressure Assessment in Spaceflight, Beatriz da Silva Pinheiro Gomes Saragoça. Instituto Superior Técnico. 2024. document. Co-advisors: Zita Martins; Edson Santos Oliveira.
  7. Autodispatcher: a fully automated failure analyzer, Daniela Domingos Vieira. FCUL; with NOS. 2024. document. Co-advisors: Salvatore Signorello; Luís Guimarãis;.
  8. Contrastive Learning For Medical Imaging, Hugo Miguel Pereira Figueiras. FCUL. 2024. document. Co-advisors: Helena Aidos.
  9. Semi-supervised brain lesion segmentation: a deep learning approach, Rodrigo Alexandre Pereira e Pinto. Instituto Superior Técnico. 2023. document. Co-advisors: Pedro Tomás, Helena Aidos.
  10. Exploiting diffusion-based data augmentation and a classifier ensemble for early wild fire detection, Gonçalo de Vasconcelos Sobral de Azeredo Falcão. Instituto Superior Técnico. 2023. document. Co-advisors: Pedro Tomás, Helena Aidos.
  11. Deep Learning for predicting disease progression of clinical endpoints in ALS, Lucas Barreto Silva. FCUL. 2023. document. Co-advisors: Sara Madeira.
  12. Adversarial Representation Learning for Medical Imaging, José David Miranda Barreira Domingues. FCUL. 2023. document. Co-advisors: Helena Aidos.
  13. Desenvolvimento de um sistema de analíticas e monitorização para uma aplicação multi-tenant SaaS, Paulo Sérgio Rodrigues Henriques. FCUL; with Create IT. 2022. document.
  14. Registration Platform For Federations And Gymnastics Clubs, Diogo de Oliveira Lopes. FCUL; with Acro Companion. 2022. document.
  15. Prognostic prediction models using Self-Attention for ICU patients developing acute kidney injury, Pedro Miguel Pereira Domingues. FCUL. 2022. document. Co-advisors: Sara Madeira.
  16. Super-resolution of biomedical images with generative adversarial networks and posterior tumor segmentation, João Luís Carrilho Guerreiro. Instituto Superior Técnico. 2022. document. Co-advisors: Pedro Tomás, Helena Aidos.
  17. Plataforma de apoio a utentes e profissionais de desporto e nutrição, Mariana Lourenço Costa. FCUL; with Xpand IT. 2022. document. Co-advisors: Sara Madeira.
  18. Automatic detection of forest fires : a deep learning approach : exploiting data augmentation for the development of early forest fire detection systems, Miguel Duarte Gonçalves. Instituto Superior Técnico. 2022. document. Co-advisors: Pedro Tomás, Helena Aidos.
  19. A study on generative augmentation with cGANs for improved chest X-ray classification, Miguel Santos Coelho Ferreira Neves. Instituto Superior Técnico. 2022. document. Co-advisors: Pedro Tomás, Helena Aidos.
  20. Human resource management aid tool, Pedro Nuno Courela Martins. FCUL; with Innowave. 2021. document.
  21. Generative deep clustering with the hierarchical and relativistic wasserstein autoencoder GAN, Gustavo Augusto Toscano Morais. Instituto Superior Técnico. 2021. document. Co-advisors: Pedro Tomás, Helena Aidos.


Selected Publications


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