Scholar

Javier Escudero (ORCID:0000-0002-2105-8725)

Google Scholar ID: sIBtm3AAAAAJ

School of Engineering. University of Edinburgh. UK

Biomedical signal processingNonlinear analysisGraph signalsComplexityMachine learning

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5,698

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Publications

20 items

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Resume (English only)

Academic Achievements

Research Experience

- Current Research Interests: Brain connectivity, Network theory, Matrix and tensor decomposition, Nonlinear analysis, Machine learning for physiological data, Alzheimer's disease and dementia, Epilepsy, Intensive care unit data
- Research Projects: In collaboration with researchers at Edinburgh, across the UK and overseas, working on the processing and analysis of biomedical signals, particularly human brain activity. By developing and applying signal processing methods, aiming at increasing our understanding of how several brain conditions progress.

Education

- Doctor of Philosophy (PhD): Applications of Blind Source Separation to the Magnetoencephalogram Background Activity in Alzheimer's Disease, Universidad de Valladolid, Award Date: 1 Jan 2010
- Master of Engineering: Universidad de Valladolid, Award Date: 1 Jan 2005

Background

- Research Interests: Brain connectivity, Network theory, Matrix and tensor decomposition, Nonlinear analysis, Machine learning for physiological data, Alzheimer's disease and dementia, Epilepsy, Intensive care unit data
- Professional Field: Biosignal processing
- Brief Introduction: His research group creates and applies data science techniques to extract information from biomedical signals and clinical time series. The main aim is to reveal the subtle changes that major diseases (e.g., Alzheimer's and epilepsy) cause in brain activity.

Miscellany