Sara C. Madeira

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Departamento de Informática

Sala/Gabinete 6.3.19

Telefone Direto 217500719
Email sacmadeira@ciencias.ulisboa.pt
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Carreira Docente Universitário
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Currículo Resumido

SARA C. MADEIRA is Associate Professor at Department of Informatics, Faculty of Sciences (FCUL), University of Lisbon, since February 2017, where she teaches courses on Data Mining, Machine Learning, Foundations of Data Science, and Intelligent Systems. She is currently coordinating the graduation in Data Science at FCUL. She is also a Senior Researcher at LASIGE, where she leads the Data and Systems Intelligence research line of excellence (RLE) and is also of a member of Health and Biomedical Informatics RLE.

From June 2009-February 2017 she was Assistant Professor at Computer Science and Engineering department, Instituto Superior Técnico (IST), University of Lisbon. She was also a senior researcher at INESC-ID, Lisbon, where she received the INESC-ID Young Research Award in 2013.

She received her PhD degree in Computer Science and Engineering at IST in 2008, her MSc degree in Computer Science and Engineering at IST in 2002, and graduated in Matemática-Informática at Universidade da Beira Interior (UBI), in 2000. She was a Lecturer/ Assistant Professor at the Informatics Department of UBI, from 2002-2008/2008-2009.

Her research interests are in the broad area of data science and include machine learning and data mining, bioinformatics and computational biology, and biomedical and health informatics. She was the PI of NEUROCLINOMICS2 – Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration (PTDC/EEI-SII/1937/2014), and is now the PI of AIpALS – Advanced learnIng models using Patient profiles and disease progression patterns for prognostic prediction in ALS (PTDC/CCI-CIF/4613/2020) and is leading FCiencias-ID/LASIGE’s team in H2020 Projects CIRCLES – Controlling mIcRobiomes CircuLations for bEtter food Systems (Grant ID: 818290) and BRAINTEASER – BRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis (Grant ID: 101017598).

She co-chaired BIOKDD’15 and BIOKDD'16, held in conjunction with SIGKDD'15 and SIGKDD'16, the premier forum for data mining researchers.

Her survey on Biclustering Algorithms for Biological Data Analysis was considered an ESI Hot Paper in Computer Science in November 2006.

She was on sabbatical leave at the Biocomputing group – University of Bologna from March 2015 to July 2016. From September 2015 to June 2016 she was an EURIAS Junior Fellow at the Istituti di Studi Avanzate in Bologna.


Interesses Científicos

Data Science. Machine Learning. Bioinformatics. Biomedical Informatics.


Scientific Interests

Machine Learning. Data Mining. Bioinformatics. Biomedical Informatics.


Publicações selecionadas
  • Rui Henriques and Sara C. Madeira, Triclustering algorithms for three-dimensional data analysis: A comprehensive survey, ACM Computing Surveys, Vol. 51, No. 5, Article 95, pp 1-45, Set 2018, ACM.
  • Rui Henriques and Sara C. Madeira, BSig: Evaluating the Statistical Significance of Biclustering Solutions, Data Mining and Knowledge Discovery (DAMI), 32(1), pp. 124–161, Jan 2018, Springer US.
  • Telma Pereira, Sandra Cardoso, Manuela Guerreiro, Alexandre Mendonça, Sara C. Madeira. Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: A case study in AD. Journal of Biomedical Informatics, 111(), Jan. 2020, Elsevier.
  • Rui Henriques and Sara C. Madeira, BicPAMS: Software for Biological Data Analysis with Pattern-based Biclustering, BMC Bioinformatics, 18(82), Fev. 2017, BioMed Central.
  • André Carreiro and Pedro M. T. Amaral and Susana Pinto and Pedro Tomás and Mamede de Carvalho and Sara C. Madeira, Prognostic Models based on Patient Snapshots and Time Windows: Predicting Disease Progression to Assisted Ventilation in Amyotrophic Lateral Sclerosis, Journal of Biomedical Informatics, 58(), pp. 133-144, Nov. 2015, Elsevier.

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