Diego Calvo Barreno

Diego Calvo Barreno
Universidad de Valladolid | UVA · Department of Theory of Signal and Communications and Telematic Engineering

Data Scientist

About

6
Publications
8,103
Reads
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117
Citations
Additional affiliations
January 2009 - present
Centro de Observación y Teledetección Espacial (COTESA)
Position
  • Scientific data
Description
  • Control of mathematical modeling techniques, machine learning, data mining. Using ETL processes large volumes of data. Experience NoSQL database such as Mongo DB, Cassandra, Neo4j, ...
August 2006 - July 2007
Telefónica I+D
Position
  • R&D Project Developer
Description
  • MyMobileWeb development cooperation, belonging to the Morfeo platform.
Education
March 2016 - February 2018
Universitat Oberta de Catalunya
Field of study
September 2006 - June 2010
September 2005 - September 2013

Publications

Publications (6)
Chapter
Full-text available
Mental Health disorders such as schizophrenia, depression, and dementia have a great impact on society worldwide. Recent developments have witnessed numerous advances in telemedicine that allow remote monitoring of elderly people with diseases of this pathology. One of key factors influencing recent advances in health information systems is the use...
Conference Paper
Full-text available
Mental Health disorders such as schizophrenia, depression, and dementia have a great impact on society worldwide. Recent developments have witnessed numerous advances in telemedicine that allow remote monitoring of elderly people with diseases of this pathology. One of key factors influencing recent advances in health information systems is the use...
Article
Full-text available
Background: Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. Objective: The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms...
Poster
Full-text available
Application of Deep Learning Techniques such as Convolutional Neural Networks to predict viruses, cancer and therapies. 1) Virus prediction. 2) Cancer prediction 3) Therapy prediction
Presentation
This study is focused on the development of a technology to identify characteristics in nucleotide sequences using deep learning provided by Convolutional Neural Networks. In order to demonstrate the effectiveness of this technology, a classifier has been developed to identify viruses in sequencing reads of 100 nucleotides, a proxy for a real NGS s...

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