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Julia Díaz García

Adjunt Professor of Computer Science and AI

Bio

PhD in Computer Science

Department of Computer Science and Engineering

I (https://es.linkedin.com/in/julia-diaz-repsol) am an accomplished professional with a background in Mathematics and the distinction of being the first Doctor of Computer Engineering from the Autonomous University of Madrid.

I have extensive experience, including roles as a part-time associate professor and director of the Health and Energy Predictive Analytics Area at the Institute of Knowledge Engineering (www.iic.uam.es).

Notably, I have received recognition for my STEM career, winning the iDanae Award (https://medal.ctb.upm.es/launch_iDanae/stem/), Silicon Awards (https://www.linkedin.com/posts/silicon-es_siliconawards2024-premiossilicon-ciberseguridad-activity-7077964274169434112-Lnuy), and CIO Spain 2023 Award (https://www.ciospain.es/industria-y-utilities/repsol-premio-al-mejor-proyecto-de-gestion-de-los-datos-e-inteligencia-digital).

Currently I am serving as the Head of Data Science at Repsol, playing a significant role in the company’s digital transformation, leading more than 300 Data & Analytics initiatives. Repsol’s digital efforts have resulted in a substantial economic impact, with an estimated increase in the coming year. I also contributes to various working groups and platforms in the field of Artificial Intelligence as IndesIA, AMETIC, COTEC, Autelsi.

Teaching

Introduction to Economic Big Data

Research Interests

Artificial Intelligence, Big Data, Machine Learning

Selected publications

  1. Gala, A. Fernández, J. Díaz, J.R. Dorronsoro. Hybrid machine learning forecasting of solar radiation values. Neurocomputing 176 (2016) 48-59. 

DOI: 10.1016/j.neucom.2015.02.07

  1. Fernández, A. M. González, J. Julia Díaz, J.R. Dorronsoro. Diffusion Maps for Dimensionality Reduction and Visualization of Meteorological Data. Neurocomputing 163 (2015) 25–37. 

DOI:10.1016/j.neucom.2014.08.090.

  1. Di Deco, A.M. González, J. Díaz, V. Mato, D. García-Frank, J. Álvarez-Linera, et al. Machine learning and social network analysis applied to Alzheimer’s disease biomarkers. Current Topics in Medicinal Chemistry, 13 (5) (2013), pp. 652–662.
  2. Pruenza, M. Teulón, L. Lechuga, J. Díaz, A. González. Development of a Predictive Model for Induction Success of Labour. Special Issue on Big Data and e-Health. http://dx.doi.org/10.9781/ijimai.2017.03.003
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