I am a Research Fellow in Digital Health at the Department of Computer Science, CRIStAL Lab, University of Lille, France. This is a collaborative project with the Lille University Hospital and INCLUDE. My current project involves developing and applying unsupervised machine learning techniques to classify patients with systemic autoimmune diseases.
Before joining the University of Lille, I was a Research Fellow in Machine Learning at the Department of Computer Science, Institute of Data Science and Artificial Intelligence, University of Exeter, United Kingdom (UK). Before this, I was a Postdoctoral Researcher with the Decision and Cognitive Sciences Research Centre, University of Manchester, UK. I hold M.Sc. and Ph.D. degrees in Computer Science from the Center for Research and Advanced Studies of the National Polytechnic Institute, Cinvestav-IPN, Mexico.
In general, my scientific expertise is focused on investigating cluster analysis methods, also known as unsupervised machine learning in Artificial Intelligence. My research consists in creating and adapting clustering approaches (e.g., multi-view clustering, biclustering) and their applications to different research fields such as digital healthcare, the labour market, and network analysis. My research currently focuses on developing integrative cluster analysis approaches to address healthcare-related data problems and help to understand better disease complications and treatment goals.
I am grateful to have been generously supported by or closely working with the following funding institutes.
An AI-based system named C3-IoC to help students explore career paths in IT according to their level of education, skills and prior experience.
An evolutionary method to multi-view clustering, the algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views.
This article explores the interaction between dissimilarity measures and internal cluster validity techniques in the context of multi-objective clustering.
This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC.
A visualizer of SSc biclusters.
A CVI toolbox for estimating the number of clusters.
A multi-view data repository.
Mapas interactivos del COVID-19 en México.
A career guidance system for assessing student skills.
Evolutionary clustering algorithms with code.