Daniel Díez Álvarez

Daniel Díez Álvarez has a diverse work experience in the fields of artificial intelligence and innovation. Daniel is currently a Co-Founder at bloXlab, a position they started in September 2022. Prior to this role, Daniel worked at bloXmove as the CIO and Head of Innovation since October 2021. Daniel also served as a Token Engineer at the same company from May 2021 to October 2021.

Before joining bloXmove, Daniel worked at Mercedes-Benz AG, where they held various positions. Daniel started as an Intern in Autonomous Driving and Computer Vision from May 2019 to December 2019. Daniel then worked as a Werkstudent or part-time AI Researcher from November 2018 to April 2019. Finally, they took on the role of an ML Engineer with a focus on Artificial Intelligence, starting in December 2019 and continuing until December 2022.

Daniel's work experience also includes a role as a Scientific Researcher at the University of Stuttgart, which they started in December 2016 and ended in December 2019.

Daniel Díez Álvarez is currently a PhD student at Brandenburgische Technische Universität Cottbus-Senftenberg, where they are pursuing a Doctor of Philosophy degree in Artificial Intelligence. Daniel began their doctoral studies in 2019 and is expected to complete them in 2022.

Prior to their PhD, Daniel earned a Master of Science degree in Big data & visual analytics from UNIR - La Universidad en Internet. Daniel attended this institution from 2018 to 2019.

Before their master's degree, Daniel completed their undergraduate education at Universidad de Valladolid, where they obtained a Grado en Ingeniería (Bachelor's degree) in Ingeniería mecánica. Further information about the start and end years of their undergraduate education is not provided.

In addition to their formal education, Daniel has obtained several certifications from Coursera. Daniel completed the courses "Structuring Machine Learning Projects," "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization," "Neural Networks and Deep Learning," and "Machine learning" in 2020, 2019, and 2018, respectively.