Validated Expert info
Expert can perform Ad hoc expertise missions at network and programme level in relation to:
Thematic expertise:
> Digital transformation
Summary
Prof. Toral ()Ph.D. Electronic Engineering) research focuses on the use of artificial intelligence algorithms based on deep learning and reinforcement learning for the adaptive deployment of unmanned vehicle fleets using environmental information. The main application cases have been transportation, disasters and environmental monitoring. Additionally, he is also expert in large language models and natural language processing, including the ethical issues related to generative AI. He is part of Stanford University names World’s Top 2% Scientists, 2023, being 1244 out of 111.935 authors in the area of Electric & Electronic Engineering. His scientific production includes a total of 110 journal publication with impact factor which are distributed per quartiles as 44/29/28/9 (Q1/Q2/Q3/Q4), being 18 of them in the first decile.
His main achievements include the application of deep reinforcement learning techniques for the monitoring of water resources, including the optimisation of the parameters of the neural network models used, the development of deployment and informative route planning techniques for autonomous surface vehicles using Bayesian optimization, and the detection of cyanobacteria in Lake Ypacarai (Asunción, Paraguay) using a fleet of three autonomous vehicles.
Prof. Toral is committed to open science and the reproducibility of research, so all his publications are openly available in the institutional repository of the University of Seville idus (idus.us.es) and the code is openly available on github.