Rita Paula Almeida Ribeiro

Rita P. Ribeiro is an Assistant Professor at the Department of Computer Science at the Faculty of Sciences of the University of Porto (FCUP) and a Senior Researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the Institute of Systems Engineering and Computing, Technology and Science (INESCTEC). Her main research interests focus on learning problems in imbalanced domains, anomaly detection, evaluation issues in learning tasks and application problems related to social good and environmental impact. She has been involved in several research projects concerning ecological problems, fraud detection and predictive maintenance applications. She is a member of the program committee of several international conferences, also serves as an editor and reviewer for several international journals and has been involved in the organization of various scientific events.

Domains

Artificial Intelligence and Decision Support

Projects

T4CDTKC

This project aims to outline an innovative training program oriented to the development of skills around the themes inherent to the i4.0 industry. Within this scope, the structure of the programme is defined, the production of the thematic contents to be explored based on the pedagogical methodology adopted. A teaching-learning methodology centred on an online platform is explored, having as central element a collection of videos complemented by a set of 'handouts' that organise the set of key messages and 'take-ways' associated to each module.

FailStopper

Early failure detection of public transport vehicles in operational context

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Publications

ECML PKDD 2018 Workshops

Monreale, A;Alzate, C;Kamp, M;Krishnamurthy, Y;Paurat, D;Sayed-Mouchaweh, M;Bifet, A;Gama, J;Ribeiro, RP;

2019

Communications in Computer and Information Science

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Kamp, M;Koprinska, I;Bibal, A;Bouadi, T;Frénay, B;Galárraga, L;Oramas, J;Adilova, L;Krishnamurthy, Y;Kang, B;Largeron, C;Lijffijt, J;Viard, T;Welke, P;Ruocco, M;Aune, E;Gallicchio, C;Schiele, G;Pernkopf, F;Blott, M;Fröning, H;Schindler, G;Guidotti, R;Monreale, A;Rinzivillo, S;Biecek, P;Ntoutsi, E;Pechenizkiy, M;Rosenhahn, B;Buckley, C;Cialfi, D;Lanillos, P;Ramstead, M;Verbelen, T;Ferreira, PM;Andresini, G;Malerba, D;Medeiros, I;Fournier-Viger, P;Nawaz, MS;Ventura, S;Sun, M;Zhou, M;Bitetta, V;Bordino, I;Ferretti, A;Gullo, F;Ponti, G;Severini, L;Ribeiro, R;Gama, J;Gavaldà, R;Cooper, L;Ghazaleh, N;Richiardi, J;Roqueiro, D;Saldana Miranda, D;Sechidis, K;Graça, G;

2021

Communications in Computer and Information Science

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

2021

Communications in Computer and Information Science

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Koprinska, I;Mignone, P;Guidotti, R;Jaroszewicz, S;Fröning, H;Gullo, F;Ferreira, PM;Roqueiro, D;Ceddia, G;Nowaczyk, S;Gama, J;Ribeiro, R;Gavaldà, R;Masciari, E;Ras, Z;Ritacco, E;Naretto, F;Theissler, A;Biecek, P;Verbeke, W;Schiele, G;Pernkopf, F;Blott, M;Bordino, I;Danesi, IL;Ponti, G;Severini, L;Appice, A;Andresini, G;Medeiros, I;Graça, G;Cooper, L;Ghazaleh, N;Richiardi, J;Saldana, D;Sechidis, K;Canakoglu, A;Pido, S;Pinoli, P;Bifet, A;Pashami, S;

2023

Communications in Computer and Information Science

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Supervised theses

AI-based models to predict the Traumatic Brain Injury outcome

Ricardo Miguel Anjo Noronha Ribeiro

UP-FCUP

Centres

Artificial Intelligence and Decision Support

Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.