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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 is a member of Data and Systems Intelligence and Health and Biomedical Informatics research lines.

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 and an Assistant Professor at the Informatics Department of UBI, from 2002-2008 and 2008-2009, respectively.

Her research interests include machine learning, data mining, bioinformatics and biomedical informatics. In this context, she was the PI of NEUROCLINOMICS – Understanding NEUROdegenerative diseases through CLINical and OMICS data (PTDC/EIA-EIA/111239/2009), a research project embracing the challenges of studying complex diseases and developing efficient and effective mining algorithms for biomedical data, using Amyotrophic Lateral Sclerosis and Alzheimer’s disease as case studies. Following this project, she is currently the PI of NEUROCLINOMICS2 – Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration (PTDC/EEI-SII/1937/2014).

She co-chaired the 14th and 15th International Workshop on Data Mining in Bioinformatics (BIOKDD’15 and BIOKDD'16), held in conjunction with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (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 Setember 2015 to June 2016 she was an EURIAS Junior Fellow at the Istituti di Studi Avanzate in Bologna.


Interesses Científicos

Machine Learning. Data Mining. Bioinformatics. Biomedical Informatics.


Scientific Interests

Machine Learning. Data Mining. Bioinformatics. Biomedical Informatics.


Publicações selecionadas
  • 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, Luís Lemos, Sandra Cardoso, Dina Silva, Ana Rodrigues, Isabel Santana, Alexandre Mendonça, Manuela Guerreiro and Sara C Madeira, Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows, BMC Medical Informatics and Decision making, pp. 1-38, Jul 2017, BioMed Central.
  • Rui Henriques and Sara C. Madeira, BicPAMS: Software for Biological Data Analysis with Pattern-based Biclustering, BMC Bioinformatics, 18(82), Fev. 2017, BioMed Central.
  • Rui Henriques and Claudia Antunes and Sara C. Madeira, A Structured View on Pattern Mining-based Biclustering, Pattern Recognition, 48(12), pp. 3941-3958, Dec. 2015, Elsevier.
  • 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.