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Diogo F. Soares

Contactos

Departamento de Informática

Sala/Gabinete 6.3.38
Ext. Principal 526338
Email dfsoares@ciencias.ulisboa.pt
Página Pessoal
Carreira Docente Universitário
Categoria Professor Auxiliar

Indicadores

Orcid
Scopus
Google Scholar
CiênciaVitae

Palavras Chave

  • Machine Learning
  • Pattern Recognition
  • Subspace Clustering
  • Health Informatics

Keywords

  • Machine Learning
  • Pattern Recognition
  • Subspace Clustering
  • Health Informatics
Currículo Resumido

Diogo F. Soares is an Assistant Professor at the Department of Informatics, Faculty of Sciences, University of Lisbon (Ciências ULisboa) and an Integrated Researcher at LASIGE Computer Science and Engineering Research Centre. He holds a PhD in Informatics from the University of Lisbon and an MSc in Data Science from the same institution. Diogo has been actively involved with LASIGE since 2019, contributing to national and international projects. His work spans multiple R&D projects, such as BRAINTEASER and AIpALS, where he played a pivotal role in developing predictive and stratification models for neurodegenerative diseases. He has significantly contributed to advancing machine learning and data mining methodologies, focusing strongly on patient-centric approaches for biomedical applications. Diogo's research bridges computational innovation and healthcare applications. He specializes in unsupervised (focusing on subspace clustering) and pattern-centric representation learning techniques, focusing on patient stratification, disease progression prediction, and clinical decision support.

Publicações selecionadas
  • Diogo F. Soares, Rui Henriques, Sara C. Madeira (2024). Comprehensive Assessment of Triclustering Algorithms for Three-Way Temporal Data Analysis. Pattern Recognition, 110303. https://doi.org/10.1016/j.patcog.2024.110303
  • Daniela M. Amaral, Diogo F. Soares, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira, Pedro Tomás & Helena Aidos, (2024). Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns. Nat Commun 15, 5717. https://doi.org/10.1038/s41467-024-49954-y
  • Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira, (2023). Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis. Scientific Reports, 13, ISSN 2045-2322. eISSN . http://dx.doi.org/10.1038/s41598-023-33223-x
  • Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira, (2022). Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis. Journal of Biomedical Informatics, 134, ISSN 1532-0464. eISSN . http://dx.doi.org/10.1016/j.jbi.2022.104172
  • Erica Tavazzi, Enrico Longato, Martina Vettoretti, Helena Aidos, Isotta Trescato, Chiara Roversi, Andreia S. Martins, Eduardo N. Castanho, Ruben Branco, Diogo F. Soares, (...), Piero Fariselli, Sara C. Madeira, Barbara Di Camillo, (2023). Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. Artificial Intelligence in Medicine, ISSN 0933-3657. eISSN . http://dx.doi.org/10.1016/j.artmed.2023.102588

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