Sara Silva

Ph.D., Associate Professor at Universidade de Lisboa, Researcher at LASIGE

Email:  my_first_name [at] fc.ul.pt

Phone: +351-217500565 (ext.26701)

My Google Scholar profile

My ResearchGate profile

ORCID: 0000-0001-8223-4799

I am an Associate Professor in Computer Science at the Department of Informatics of the Faculty of Sciences of the University of Lisbon and a senior researcher at LASIGE.

I work in machine learning, mostly on methods based on evolution and natural selection, like genetic programming. I am interested in improving learning through feature engineering, avoiding overfitting by reducing model behavioral complexity, and producing trustworthy predictions with enriched model outputs. My main application domains are remote sensing and biomedical informatics.

Highlights

Co-author of Lectures on Intelligent Systems. General Chair of GECCO 2023. Member of the executive boards of ACM SIGEVO and SPECIES. Recipient of the EvoStar Award 2018. Associate editor of SWEVO, GPEM, ACM TELO. Member of the PC of IJCAI, GECCO, EuroGP. Creator of GPLAB.

Selected Publications

L Vanneschi, S Silva (2023). Lectures on Intelligent Systems. Natural Computing Series, Springer.

JE Batista, S Silva (2023). Evolutionary Machine Learning in Environmental Science. Handbook of Evolutionary Machine Learning, 563-590.

RT Sousa, S Silva, C Pesquita (2023). Explainable Representations for Relation Prediction in Knowledge Graphs. International Conference on Principles of Knowledge Representation and Reasoning (KR2023), 635-646.

NM Rodrigues, KM Malan, G Ochoa, L Vanneschi, S Silva (2023). Fitness landscape analysis of convolutional neural network architectures for image classification. Information Sciences 609, 711-726.

L Muñoz, L Trujillo, S Silva (2020). Transfer learning in constructive induction with genetic programming. Genetic Programming and Evolvable Machines 21(4), 529-569.

W La Cava, S Silva, K Danai, L Spector, L Vanneschi, JH Moore (2019). Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation 44, 260-272.

S Silva, L Vanneschi, AIR Cabral, MJ Vasconcelos (2018). A semi-supervised genetic programming method for dealing with noisy labels and hidden overfitting. Swarm and Evolutionary Computation 39, 323-338.

Recent PhD Students

João E. Batista
Nuno M. Rodrigues
Jessica Catarino
Rita T. Sousa (co-advisor)
Paulo Santos (co-advisor)
Breno Sousa (co-advisor)