Sara Silva


Laboratório de Sistemas Informáticos de Grande Escala (LASIGE)

Sala/Gabinete 6.3.14
Ext. Principal 26701
Telefone Direto 217500565
Página Pessoal

Carreira Investigacao
Categoria Emprego Cientifico Individual 2017


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Currículo Resumido

Sara Silva obtained a BSc and MSc in Informatics at the University of Lisbon, and a PhD (2008) in Informatics Engineering at the University of Coimbra, Portugal. Her main research interests are bio-inspired machine learning methods for data mining, like neural networks, genetic algorithms, and particularly genetic programming, which she has applied in several interdisciplinary projects ranging from remote sensing and forest science to epidemiology and medical informatics.

Sara Silva has around 70 peer-reviewed scientific publications, 10 of which distinguished with nominations and international awards. She is a member of the editorial board of the Genetic Programming and Evolvable Machines journal, and has been chair of several international conferences on Evolutionary Computation. In 2015 she was Editor-In-Chief of the Genetic and Evolutionary Computation Conference (GECCO), the largest world conference on this field. In 2018 she received the EvoStar Award for Outstanding Contribution to Evolutionary Computation in Europe. She is the creator and developer of GPLAB - A Genetic Programming Toolbox for MATLAB.

Scientific Interests

Genetic Programming, other flavours of Evolutionary Computation and all other biologically inspired methods.

Applications, mostly in Biomedical Informatics and Remote Sensing.

Publicações selecionadas
  • A Study of Generalization and Fitness Landscapes for Neuroevolution (2020). NM Rodrigues, S Silva, L Vanneschi, IEEE Access 8, 108216-108234.
  • Evolving knowledge graph similarity for supervised learning in complex biomedical domains (2020). RT Sousa, S Silva, C Pesquita. BMC bioinformatics 21 (1), 6.
  • Transfer learning in constructive induction with Genetic Programming (2019). L Munoz, L Trujillo, S Silva. Genetic Programming and Evolvable Machines, 1-41.
  • Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees (2018). AIR Cabral, S Silva, P Silva, L Vanneschi, MJP Vasconcelos. ISPRS Journal of Photogrammetry and Remote Sensing.
  • A semi-supervised Genetic Programming method for dealing with noisy labels and hidden overfitting (2018). S Silva, L Vanneschi, AIR Cabral, MJ Vasconcelos. Swarm and Evolutionary Computation.

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