Miguel Angel Prada

I am a postdoctoral researcher in the Aalto University School of Science and Technology and the Parsimonious Modelling group at HIIT since September 2009. I work in ISMO, a multidisciplinary project about Intelligent Structural Health Monitoring Systems.

I studied Computer Engineering at the University of León (Spain). Later, I completed my Doctorate degree while working in the Suppress group of the University of León (see also the Automatic control area). The research lines of this group focus on monitoring of industrial processes. During my studies, I also made a short research visit to CNEL (UF).

The topic of my thesis was the application of self-organizing maps for the analysis of the dynamic behavior of industrial processes from data.

Research interests

My main research interest is the application of data analysis to structural health monitoring and industrial processes. I am especially interested in the following topics: exploratory data analysis and visualization, dimensionality reduction, novelty/anomaly detection and neural networks.

Contact information

E-mail: mpradacis.hut.fi

Room A317
Aalto University School of Science and Technology
Department of Information and Computer Science
Konemiehentie 2, Espoo
P.O. Box 15400, FI-00076 Aalto
Finland

Publications

Miguel A. Prada, Jaakko Hollmén, Janne Toivola, and Jyrki Kullaa. Three-way analysis of structural health monitoring data. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages 256-261, IEEE, August 2010.

Janne Toivola, Miguel A. Prada, and Jaakko Hollmén. Novelty detection in projected spaces for structural health monitoring. Lecture Notes in Computer Science (LNCS), Advances in Intelligent Data Analysis IX, vol. 6065, pages 208-219, May 2010.

Juan J. Fuertes, Manuel Domínguez, Perfecto Reguera, Miguel A. Prada, Ignacio Díaz, Abel A. Cuadrado. Visual dynamic model based on Self-Organizing maps for supervision and fault detection in industrial processes. Engineering Applications of Artificial Intelligence, Vol. 23, Issue 1, pages 8-17, February 2010.

Ignacio Díaz, Abel A. Cuadrado, Juan J. Fuertes, Manuel Domínguez, Miguel A. Prada. Visualization of MIMO Process dynamics using Local Dynamic Modelling with Self Organizing Maps. Engineering Applications of Neural Networks, pages 119-130, Communications in Computer and Information Science series, vol. 43. Springer, 2009.

Manuel Domínguez, Juan J. Fuertes, Perfecto Reguera, Miguel A. Prada, Serafín Alonso, Antonio Morán, Fernando Briz, Alberto B. Díez. Tool for instruction and learning on variable speed drives through the Internet. European Control Conference 2009. Budapest, Hungary.

Miguel A. Prada. Técnicas de extracción del conocimiento basadas en data mining visual para la supervisión de procesos industriales. Análisis de la dinámica basado en mapas auto-organizados (In Spanish). Doctoral Thesis, University of León, Spain, June 2009.

M. Domínguez, J.J. Fuertes, P. Reguera, M.A. Prada, A. Morán, Inter-University Network of Remote Laboratories, Proceedings of the 17th IFAC World Congress 2008, Seoul, South Korea, July 2008. ISBN: 978-3-902661-00-5.

Juan J. Fuertes, Miguel A. Prada, Manuel Domínguez, Perfecto Reguera, Ignacio Díaz, Alberto B. Díez. Visualization of dynamic parameters of a multivariable system using Self-Organizing Maps, Proceedings of the 17th IFAC World Congress 2008, Seoul, South Korea, July 2008. ISBN: 978-3-902661-00-5.

Juan J. Fuertes, Miguel A. Prada, Manuel Domínguez, Perfecto Reguera, Ignacio Díaz, Abel A. Cuadrado-Vega. Modeling of Dynamics using Process State Projection on the Self Organizing Map. Lecture Notes in Computer Science (LNCS), 4668, pages 589-598, August 2007.

Juan José Fuertes, Miguel Angel Prada, Manuel Domínguez, Perfecto Reguera, Ignacio Díaz. Remote Supervision of Industrial Processes based on PMML-defined SOM Models. Proceedings of the ECC07, European Control Conference 2007. Kos, Greece.

The publications at Aalto University are also listed in bibdb.