Anton Aguilar

Ico_CTTC
Ruben Anton Aguilar | CTTC

Sustainable Artificial Intelligence (SAI)

Phd , Researcher

Phone: +34 93 645 29 00

Anton Aguilar-Rivera holds a PhD on Intelligent systems PhD degree of Information Technologies and Communications, major in Intelligent Systems from Tecnológico de Monterrey (2015). He also holds a Master degree in automation and a bachelor degree on Electronics and Communications Engineering.  His PhD dissertation is about  multi-objective evolutionary algorithms methods to perform on-line multi-period portfolio optimization for stock market investment. Before starting his PhD he used to work in a steel factory as a furnace engineer. He has experience as a software developer in the transportation industry as well.

After his PhD, Anton came to Europe to work in as a postdoc fellow in Barcelona Supercomputer Center, where he worked in  the implementation of HPC development of AI algorithms for real-time applications. Afterwards he went to the UK to work in the Bristol Robotics Laboratory as a researcher. There, he worked in the CAVForth project, performing research and development on safety verification of autonomous vehicles.

Anton is currently back to Barcelona, working in the CTTC at the 5GMed project. This project is about developing AI-based services for 5G networks in cross-border scenarios.

Some research interests: Artificial Intelligence, Optimization, Machine Learning, HPC, autonomous vehicles, evolutionary computation, deep-learning, CARLA simulator, CUDA, Linux, Data science, software development, algorithmic trading.

The unscented genetic algorithm for fast solution of GA-hard optimization problems
APPLIED SOFT COMPUTING. Vol. 139. April 2023.
Aguilar-Rivera A.
10.1016/j.asoc.2023.110260 Google Scholar
Evaluation of AI-based Smart-Sensor Deployment at the Extreme Edge of a Software-Defined Network
2022 Ieee Conference On Network Function Virtualization And Software Defined Networks, Nfv-Sdn 2022 - Proceedings. pp. 1-5 January 2022.
Aguilar-Rivera, A, Vilalta, R, Parada, R, Perez, FM, Vazquez-Gallego, F
10.1109/NFV-SDN56302.2022.9974729 Google Scholar
A GPU fully vectorized approach to accelerate performance of NSGA-2 based on stochastic non-domination sorting and grid-crowding
APPLIED SOFT COMPUTING. Vol. 88. March 2020.
Aguilar-Rivera, Anton
10.1016/j.asoc.2019.106047 Google Scholar
Bit-wise Pseudo-Bayes genetic algorithms to model data distributions
APPLIED SOFT COMPUTING. Vol. 64. pp. 550-563 March 2018.
Aguilar-Rivera, A
10.1016/j.asoc.2017.12.034 Google Scholar
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