Nova-Lincs Group: Intelligent Systems Research Topics: Artificial intelligence; Machine learning; Deep learning; High performance computing Google scholar: https://scholar.google.ca/citations?user=9mKfTocAAAAJ&hl=vi Helder Daniel received his PhD in Electronic Engineering and Computation from the University of Algarve in 2004, for his research work on automatic parallelization of matrix algorithms. Currently he is a faculty member at the Faculty of Sciences and Technology of University of Algarve. In the past he served as President of the Pedagogical Council (2018) and as Director of the Undergraduate Informatics Engineering programme (2016-2018), all at the University of Algarve. His main research interests are artificial intelligence, deep learning and high performance computing. Recent and ongoing research includes time series analysis and characterization, applied on fault identification, while a researcher with the following projects: Deep generative models for data augmentation in fault detection 2023.10664.CPCA.A0 Co-Principal Investigator (Co-PI) Multimodal Deep Leaning of Deterioration and Fault Prognosis for Wind Turbine Drivetrains MOST: 2016YFE0132200 Researcher Selected Publications: Daniel, H., S. Baltazar, J. Oliveira (2023). Wind turbine gearbox fault identification with 1D CNNs [Poster]. Encontro da Rede Nacional de Computação Avançada 2023, Universidade de Trás-os-Montes e Alto Douro, Portugal. Baltazar S., J. Oliveira, H. Daniel, C. Li (2018) A review on neurocomputing based wind turbines fault diagnosis and prognosis. International Prognostics and Health Management Conference, Chongqing, China Leila, Azari, A. M. Rahmani, H. A.Daniel, N. N. Qaderd (2018), A data replication algorithm for groups of files in data grids, Journal of Parallel and Distributed Computing vol. 113, pp 115-126, https://doi.org/10.1016/j.jpdc.2017.10.008 Oliveira, J., S. Baltazar e H. Daniel (2017). On asynchronous parallelization of order-based GA over grid-enabled heterogenous commodity hardware, Soft Computing, vol. 21, pp. 6351–6368. Springer. https://doi.org/10.1007/s00500-016-2190-2 Daniel, H. e A. Ruano (2004). Speeding up a learning algorithm for multilayer perceptrons using the MAPS environment, Proc. 6th Portuguese Conference on Automatic Control (Controlo 2004), Faro, Portugal, Vol. 2, pp. 399-404