Daniel Salvador Urgel

Daniel Salvador Urgel has worked for five companies throughout their career. In 2021, they began working as an NLP Engineer for Savana, where they designed a NLP Module for detecting and inferencing temporal information about medical entities, as well as a pipeline for Named Entity Recognition and Linking of medical concepts with a knowledge base. From 2017 to 2021, they worked at everis as a Lead Data Scientist and Data Scientist, where they created a services platform using cloud providers, Docker and Kubernetes, increased the business value of a technology radar product through unstructured text information enrichment, clustering and data visualization techniques, and deployed a virtual shopping experience on a retail client supermarket in Prague. In 2016, they worked as an IT Junior Architect at Schindler Group, where they established processes and developed a product lifecycle tool, as well as defined business and technical requirements with different headquarters and countries. In 2015, they worked as a Research Intern at Fundación CIRCE, where they collaborated on research about the remote control and monitoring of electric car charging stations within the SIRVE project and reduced the cost of monitoring electric vehicle charges using an application instead of expensive integrated hardware. Finally, in 2014, they participated in the Demola Program.

Daniel Salvador Urgel has an extensive education history. From 2012 to 2016, they obtained a Bachelor's Degree in Computer Engineering from the Universidad de Zaragoza. From 2014 to 2015, they attended the Tampere University of Technology and obtained a Bachelor's Degree in Computer Engineering. In 2017, they obtained a Master's Degree in Artificial Intelligence Research from the Universidad Internacional Menéndez Pelayo. Additionally, they have obtained three certifications: IBM Certified Application Developer - Watson V3 Certification from IBM Professional Certification in February 2018, Neural Networks and Deep Learning from Coursera in October 2017, and Machine Learning by Stanford University from Coursera in September 2017.