Data Science

NOVA Math > Thematic Lines > Data Science

About

Nowadays, the volume of big data is growing enormously, challenging scientists to provide new ways of organising and analyse it, in order to be able to extract hidden knowledge in the data, and build new automated decision and intelligent reasoning systems.

Solutions to these challenges require expertise from multidisciplinary areas, including statistics, optimization, and computer science, for the efficient processing of large volumes of data, aiming to support decision making including, classification tasks, forecasting, optimization, and visualization.

The Thematic Line Data Science results from an effort to promote interdisciplinary research between NOVA Math and other R&D units and is taking its first steps towards what we expect to be an area of great social impact.

Marta Isabel Belchior Lopes

Data Science Coordinator

Publications

An extended delayed weighted gradient algorithm for solving strongly convex optimization problems
2022, JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, Andreani,R;Oviedo,H;Raydan,M;Secchin,LD
ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data
2022, STATISTICAL METHODS IN MEDICAL RESEARCH, Jensch,A;Lopes,MB;Vinga,S;Radde,N
Stability of principal components under normal and non-normal parent populations and different covariance structures scenarios
2022, JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, Bispo,R;Marques,F
Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
2022, KNOWLEDGE-BASED SYSTEMS, Cruz,RC;Costa,PR;Krippahl,L;Lopes,MB
Discriminant analysis of distributional data via fractional programming
2021, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Dias,S;Brito,P;Amaral,P
A Maximal Margin Hypersphere SVM
2021, COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT V, Malha,R;Amaral,P
Robust identification of target genes and outliers in triple-negative breast cancer data
2019, STATISTICAL METHODS IN MEDICAL RESEARCH, Segaert,P;Lopes,MB;Casimiro,S;Vinga,S;Rousseeuw,PJ

Projects

MONET: Multi-omic networks in gliomas
FCT - Fundação para a Ciência e a Tecnologia, I.P., Marta B. Lopes, Jorge Orestes Cerdeira, Marcos Raydan
AI-4-MUFF: Artificial intelligence on the management of the degree of readiness in urban firefighting
FCT - Fundação para a Ciência e a Tecnologia, I.P., Isabel Gomes, Jorge Orestes Cerdeira, Filipe Marques,...
MATISSE: A machine learning based forecasting system for shellfish safety
FCT - Fundação para a Ciência e a Tecnologia, I.P., Marta B. Lopes