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Biography
Computer Science graduate from the University of Cordoba, specialised in software development. Completed a Master's degree in Telematics and Telecommunication Networks at the University of Malaga in 2020. Currently completing a PhD in Computer Science focused on the identification of population patterns using advanced machine learning techniques applied to mobile phone and geolocation data (thesis deposited, defence expected April 2026).
Interests
- Mobility Analysis
- Machine Learning
- Mobile Phone Data Analytics
- Multi-label Classification
- Geospatial Data Science
Education
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Graduate in Computer Science, 2015-2019
University of Cordoba
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Master degree in Telematics and Telecommunication networks, 2020
University of Malaga
Skills
Python
Advanced knowledge
Amazon Web Services
Basic knowledge
English
Advanced level (Cambridge FCE with merits)
C, C++, C#
Advanced knowledge
Experience
PhD Researcher
Identification of population patterns using advanced machine learning
techniques applied to mobile phone and geolocation data
RCT Development Intern
Adquired knowledge on C#, Visual Studio, test cases and 5G development.
Research scholarship
Adquired knowledge on Python, machine learning and classification tasks.
Accomplishments
Projects
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Recent & Upcoming Talks
Recent Publications
(2026).
A new local proximity-based k-nearest neighbours method for multi-label
population patterns identification using mobile phone data.
Information Processing & Management (under revision).
(2026).
Sensing labour mobility flows of cross-border urban regions using
machine learning and geolocated social network data.
EPJ Data Science (under revision).
(2026).
MAPLID: a new multi-label approach for place identification using data
supplied by mobile network operators.
International Journal of Geographical Information Science, 1–24.
(2025).
A supervised approach for land use identification in Trento using mobile
phone data as an alternative to unsupervised clustering techniques.
Applied Sciences, 15(4), 1753.
(2024).
SAMPLID: A new supervised approach for meaningful place identification
using call detail records as an alternative to classical unsupervised clustering techniques.
ISPRS International Journal of Geo-Information, 13(8), 289.
(2024).
False flag: An evolutionary false labeling approach for multi-label
classification.
Applied Soft Computing (accepted).
(2022).
Local-based k values for multi-label k-nearest neighbors rule.
Engineering Applications of Artificial Intelligence, 116, 105487.