Scholar
Armel Soubeiga
Google Scholar ID: sbJ57XQAAAAJ
PhD
Citations & Impact
Citations
12
H-index
2
i10-index
0
Publications
19
Co-authors
8
list available
Publications
Resume (English only)
Academic Achievements
- Papers: 'Comparative analysis of multidimensional sequential trajectories clustering methods', Preprint submitted to Pattern Recognition; 'Evidential clustering with view-weight learning for proximity data', Preprint submitted to Neurocomputing – under review; Conference: 'Soft-ECM: An extension of Evidential C-Means for complex data', accepted to FUZZ-IEEE, 2025; Best doctoral paper award: 'Clustering multi-relationnel flou des trajectoires de la douleur chronique', LFA 2024; Paper: 'Multi-View Relational Evidential C-Medoid Clustering with Adaptive Weighted', 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA); Paper: 'Clustering and Interpretation of Time-series Trajectories of chronic pain using Evidential c-means', Expert Systems With Applications.
Research Experience
- Currently an R&D Machine Learning, GenAI Engineer at Safran Aircraft Engines, and a Postdoctoral Researcher at Etis Laboratory. Previously worked as a data scientist at RCTs in Lyon for three years, developing tools for analyzing care pathways using hard and fuzzy clustering techniques, dimensionality reduction, variable scoring, and interactive visualization. Before joining RCTs, served as a data analyst consultant for the World Bank in Burkina Faso.
Education
- Completed a PhD in Computer Science at the University of Clermont Auvergne, within the Doctoral School of Engineering Sciences, in collaboration with LIMOS and Simon Fraser University (SFU), under the supervision of Jonas KOKO, Violaine ANTOINE, and Sylvain MORENO. The thesis focused on the development of unsupervised and interpretable clustering methods, particularly relational and multi-view evidential (uncertainty-based) clustering.
Background
- Research interests include GenAI and Multimodal LLM Agents, Uncertainty Quantification, and Explainable AI. Specializes in both applied artificial intelligence and fundamental research, with high programming skills.
Miscellany
- Proficient in Python, R, and SQL; likes TensorFlow, PyTorch, Scikit-Learn, Keras, Rshiny, Django, Flask frameworks; authored 'Web scraping with R' and actively contributes to open source software.
