CID’s research area is Artificial Intelligence. Research concerns statistical learning, managing uncertainty, and knowledge engineering with its applications in knowledge capitalization, recommender systems, and the scripting of virtual environments.
Research topics
Machine and statistical learning
Transfer learning
Uncertainty quantification
Learning from imperfect data
Model and output explainability
Integration of prior knowledge (physical, expert-based)
Reasoning and Uncertainty
Planning and decision-making (multi-criteria, under uncertainty)
Information fusion
Theories of uncertainty (belief functions, imprecise probabilities)
Logical languages (epistemic logic, non-monotonic logic, description logic)
Knowledge representation and management
System adaptation and personalization
User preference and profile identification
Context awareness through ontological modeling
Replanning and revision
Generation of personalized explanations
Virtual environment scenario design
Cognitive agent modeling
Selected publications 2025
Uncertainty Measures in a Generalized Theory of Evidence Fuzzy Sets and Systems , 2025, 520, pp.109546. ⟨10.1016/j.fss.2025.109546⟩ Credal ensembling in multi-class classification Machine Learning , 2025, 114 (1), pp.19. ⟨10.1007/s10994-024–06703‑y⟩ Advances in the Reliability Analysis of Coherent Systems under Limited Data with Confidence Boxes ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering , 2025, 11 (1), ⟨10.1061/AJRUA6.RUENG-1380⟩ Multi-view evidential K‑NN classification Information Fusion , 2025, 120, pp.103113. ⟨10.1016/j.inffus.2025.103113⟩ Soft learning probabilistic circuits International Journal of Approximate Reasoning , 2025, 185, pp.109467. ⟨10.1016/j.ijar.2025.109467⟩ Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation Information Fusion , 2025, 113, pp.102648. ⟨10.1016/j.inffus.2024.102648⟩ Evidential time-to-event prediction with calibrated uncertainty quantification International Journal of Approximate Reasoning , 2025, 181, pp.109403. ⟨10.1016/j.ijar.2025.109403⟩ Integrating context and criteria: a multi-head attention-based approach for multi-criteria group recommender systems World Wide Web , 2025, 28 (4), pp.50. ⟨10.1007/s11280-025–01358‑8⟩ Uncertainty Quantification in Regression Neural Networks using Evidential Likelihood-based Inference International Journal of Approximate Reasoning , 2025, pp.109423. ⟨10.1016/j.ijar.2025.109423⟩ Feature Selection Effect on Context-Aware Teacher-Support Systems Journal of Multi-Criteria Decision Analysis , 2025, 32 (2), pp.e70014. ⟨10.1002/mcda.70014⟩ Cautious classifier ensembles for set-valued decision-making International Journal of Approximate Reasoning , 2025, 177, pp.109328. ⟨10.1016/j.ijar.2024.109328⟩ Enhancing Context-Aware Recommender Systems Through Deep Feature Interaction Learning Journal of Multi-Criteria Decision Analysis , 2025, 32 (1), ⟨10.1002/mcda.70012⟩
