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    Andre Santos Ribeiro

    Andre Santos Ribeiro

    PhD Student - Neuroimaging

    London, United Kingdom

    I am a third year PhD student (currently finishing) at the Centre for Neuropsychopharmacology, Imperial College of London, United Kingdom. I am currently improving and developing...Read moreI am a third year PhD student (currently finishing) at the Centre for Neuropsychopharmacology, Imperial College of London, United Kingdom. I am currently improving and developing new methods for non-linear image registration. This project has multidisciplinary implications such as an increased confidence in neuroimaging results, which can lead to a better understanding of the human brain and the development of better biomarkers for neurological diseases such as Alzheimer's or Parkinson's Diseases.

    In 2012 I recieved the degree of Master in Biomedical Engineering and Biophysics, from the University of Lisbon, Portugal. During my degree I was involved in various projects both in Portugal and abroad: In my Bsc project I trained a feedforward neural network to predict intention of movement in impaired patients at the Imperial College London, while in my Msc project I trained various neural network architectures to estimate attenuation maps to be used for AC in PET/MR scanners at the Forschungszentrum Jülich in Germany.

    In 2012 after finishing my studies, I initiated a project at the Institute of Biophysics and Biomedical Engineering in Lisbon in which I was responsible by the development (hardware+software) of a portable and low-cost device for biomedical signal acquisition with interface to Android systems. At the same year I was assistant at the Signal Processing laboratory classes of the Faculty of Sciences of the University of Lisbon. In 2013 I worked at the Institute of Biophysics and Biomedical Engineering in a brain connectivity study combining the techniques of sMRI, fMRI, DTI and PET.

    My research interests include Medical Imaging analysis (sMRI, fMRI, DTI, PET), Brain Connectivity (structural, functional and effective) and Machine Learning (Neural networks, genetic algorithms, random forests, deep learning).

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