Short Bio

SARA C. MADEIRA is an Associate Professor at the Department of Informatics of Faculdade de Ciências, Universidade de Lisboa (Ciências), since February 2017, where she teaches/taught courses on Data Mining, Machine Learning, Foundations of Data Science, and Introduction to Research in Data Science. She’s also a Senior Researcher at LASIGE, where she coordinates the Data and Systems Intelligence Research Line of Excellence (RLE). She is also a member of the Health and Biomedical Informatics RLE. From May 2018 to January 2022 she coordinated Msc in Data Science / Post-Graduation in Data Science at FCUL. She was awarded the Scientific Prize Universidade de Lisboa/Caixa Geral de Depósitos 2021 in the area of Computer Science and Engineering (Ciências da Computação e Engenharia Informática). She received Honourable Mentions of the same prize in 2020 and 2016. She was awarded the Best Runner-Up Outreach Initiative 2020 by LASIGE.

From June 2009-February 2017 she was an Assistant Professor at the Computer Science and Engineering department at Instituto Superior Técnico (Técnico), University of Lisbon, where she taught undergraduate courses on algorithms and data structures and graduate courses on computational biology and integrative bioinformatics. She was also a senior researcher at INESC-ID, Lisbon, where she received the INESC-ID Young Research Award in 2013. She received Honourable Mentions at the Young Researchers Award promoted by Universidade Técnica de Lisboa/Caixa Geral de Depósitos in the area of Computer Science and Engineering in 2010 and 2011.

She received her PhD degree in Computer Science and Engineering from Técnico in 2008, her MSc degree in Computer Science and Engineering from Técnico 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.

She was on sabbatical leave at the Biocomputing group – the 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.

She co-chaired the 14th International Workshop on Data Mining in Bioinformatics (BIOKDD’15) and the 15th International Workshop on Data Mining in Bioinformatics (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. She also co-chaired the thematic track Computational Methods in Bioinformatics and Systems Biology (4th edition) at EPIA 2015, the XVII Portuguese Conference on Artificial Intelligence and the Bioinformatics Session at INForum 2016, and the PhD Track on the Symposium on Intelligent Data Analysis (IDA) 2021.

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. In this context, she was the PI of NEUROCLINOMICS – Understanding NEUROdegenerative diseases through CLINical and OMICS data (PTDC/EIA-EIA/111239/2009) and NEUROCLINOMICS2 – Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration (PTDC/EEI-SII/1937/2014), embracing the challenges of studying complex diseases and developing efficient and effective algorithms for heterogeneous biomedical data analysis, using Amyotrophic Lateral Sclerosis and Alzheimer’s disease as case studies. Following these projects, she 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)She further participated and participates in several other national research projects in bioinformatics and data science topics.

She is now leading FCiencias-ID/LASIGE’s team in H2020 Project CIRCLES – Controlling mIcRobiomes CircuLations for bEtter food Systems (Grant agreement ID: 818290) and H2020 Project BRAINTEASER – BRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis (Grant agreement ID: 101017598). In BRAINTEASER she leads the work package targeting patient stratification according to their phenotype assessed all over the disease evolution, and actively participates in all others tasks concerning data science/artificial intelligence tasks. In particular, the development of advanced machine learning models to unravel disease mechanisms, predict disease progression, and suggest interventions that can delay disease progression, where patient stratification is key given patient heterogeneity both in Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS).

Her 2004 paper Biclustering Algorithms for Biological Data Analysis: a Survey on ACM/IEEE TCBB, considered an ESI Hot Paper in Computer Science in November 2006, has now more than 2500 citations. Biclustering and triclustering algorithms together with their applications in biomedical data analysis are still her main research topics. She proposed state of the art biclustering algorithms based on efficient string processing and mining techniques, in the case of biclustering temporal data, and pattern mining algorithms, in the general case of biclustering tabular and network data, and co-authored the paper Triclustering Algorithms for Three-Dimensional Data Analysis: A Comprehensive survey published in the end of 2018 in ACM Computing Surveys. She further made relevant contributions in the area of prognostic prediction using machine learning in neurodegenerative diseases, in particular Amyotrophic Lateral Sclerosis (ALS) and Alzheimer’s Disease (AD). Please check her publications for details.