Jose Roberto Ayala Solares is a Machine Learning Scientist at the George Institute for Global Health at the University of Oxford. His research involves employing and developing machine learning algorithms that can find patterns in large multi-modal data to innovate and provide solutions to complex health problems.
He received his PhD from the Department of Automatic Control and Systems Engineering at The University of Sheffield in the United Kingdom in 2017 focusing on Machine Learning and Data Mining for Environmental Systems Modelling and Analysis under the supervision of Dr Hua-Liang Wei. His work focused mainly on adapting and developing a new Nonlinear AutoRegressive with eXogenous inputs (NARX) framework based on a system identification approach to analyse environmental data. Such a framework has been employed to analyse the Atlantic Meridional Overturning Circulation (AMOC) anomaly, and global magnetic disturbances in near-Earth space.
He got his MSc in Applied Mathematics and Computational Science from the King Abdullah University of Science and Technology (KAUST) in the Kingdom of Saudi Arabia and his BSc in Mechatronics Engineering with Summa Cum Laude from the Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM) in Mexico City.
- Predictive Modelling
- Statistical, Machine and Deep Learning
- Nonlinear System Identification
- Data Mining
- Data Visualisation