Sustainable Artificial Intelligence Laboratory

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Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.

Our Common Future (The Brundtland Report), United Nations ( 1987)

The Sustainable Artificial Intelligence Laboratory (SAIL) was launched in January 2020. It brings together researchers interested in different aspects of sustainability and how they can be addressed by applying Artificial Intelligence.

SAIL’s objectives are aligned to the UN’s Sustainable Development Goals. Specifically, our research work is focused on the following goals:

  • Affordable and Clean Energy
  • Industry, Innovation, Infrastructure
  • Sustainable Cities and Communities
  • Climate Action
  • Peace, Justice and Strong Institutions

We produce trustworthy and responsible AI technologies that contribute to make a more sustainable world.


Members

Principal Researcher

Dr Juan Gómez Romero

Researchers

Dr Miguel Molina Solana

PhD Students

Julio Barzola Monteses

Carlos Fernández Basso

Roberto Morcillo Jiménez

Patricia Saldaña Taboada

MSc Students

Current
  • Andrea Morales Garzón
  • Álvaro Fernández García
  • Jesús Mesa González
  • Alejandro Campoy Nieves
  • Antonio Parri González
  • Rafael Hernández Pérez Palacio
Past
  • Manuel Jesús Sánchez Casimiro-Soriguer
  • Jorge A. Bonilla Bohórquez
  • Francisco Luque Castillo
  • Juan Carlos Navarrete Solana
  • Ramón Gago Carrera

Collaborators

Prof Waldo Fajardo Contreras (Universidad de Granada)

Dr Javier Valls Prieto (Universidad de Granada)

Dr Fernando Bobillo Ortega (Universidad de Zaragoza)

Prof Jesús García Herrero (Universidad Carlos III de Madrid)

Prof Yike Guo (Imperial College London)

Dr Rossella Arcucci (Imperial College London)

Dr Julio C. Amador Díaz López (Imperial College London)

Contact

  • Daniel Saucedo Aranda s/n, Granada, 18071
  • Department of Computer Science and Artificial Intelligence

Recent Publications

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Towards self-adaptive building energy control in smart grids

We pursue to develop new Deep Learning and Reinforcement Learning methods, algorithms and tools to address three key issues – …