AJ
Adán José-García
Adán José-García
Postdoctoral researcher in Machine Learning & Digital Health
Université de Lille
53%
Libre accès
17
Documents
Affiliations actuelles
- 374570
- 410272
- 500804
Identifiants chercheurs
- adanjoga
- 0000-0003-2623-5206
- Google Scholar : H_sV5nkAAAAJ
Site web
- https://adanjoga.github.io/
Présentation
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.
Domaines de recherche
Informatique [cs]
Publications
Multi-view Clustering of Heterogeneous Health Data: Application to Systemic SclerosisParallel Problem Solving from Nature – PPSN XVII, Sep 2022, Dortmund, Germany. pp.352-367, ⟨10.1007/978-3-031-14721-0_25⟩
Communication dans un congrès
hal-03790502v1
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A survey of cluster validity indices for automatic data clustering using differential evolutionGECCO '21: Genetic and Evolutionary Computation Conference, Jul 2021, Lille, France. pp.314-322, ⟨10.1145/3449639.3459341⟩
Communication dans un congrès
hal-03271939v1
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On the Interaction Between Distance Functions and Clustering Criteria in Multi-objective ClusteringEvolutionary Multi-Criterion Optimization, Mar 2021, Shenzhen, China. pp.504-515, ⟨10.1007/978-3-030-72062-9_40⟩
Communication dans un congrès
hal-03271937v1
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Many-view clustering: an illustration using multiple dissimilarity measuresGECCO '19: Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.213-214, ⟨10.1145/3319619.3323365⟩
Communication dans un congrès
hal-03271938v1
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Evolutionary Clustering Using Multi-prototype Representation and Connectivity CriterionMexican Conference on Pattern Recognition, Jun 2017, Huatulco, Mexico. pp.63-73, ⟨10.1007/978-3-319-59226-8_7⟩
Communication dans un congrès
hal-03271940v1
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Building topic maps from relational databasesInternational Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2012), Sep 2012, Mexico City, Mexico. pp.1-6, ⟨10.1109/ICEEE.2012.6421192⟩
Communication dans un congrès
hal-03271941v1
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Biclustering Algorithms Based on Metaheuristics: A ReviewMansour Eddaly; Bassem Jarboui; Patrick Siarry. Metaheuristics for Machine Learning. New Advances and Tools, Springer Nature Singapore, pp.39-71, 2023, Computational Intelligence Methods and Applications, ⟨10.1007/978-981-19-3888-7_2⟩
Chapitre d'ouvrage
hal-03790510v1
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