natural language processing

Interpretable sentiment-aware transformer-based model for individual log anomaly detection in distributed systems using word-level explanations

An interpretable, sentiment-aware transformer model (BERT-ITPT-FiT) combined with SHAP for parser-free, word-level explainable anomaly detection in individual log entries.

XV Chilean Conference on Operations Research

Our team participated in OPTIMA 2025 with three contributions across healthcare operations, observatory log analytics, and industrial maintenance. Presentations covered robust routing for home hospitalization, decision oriented relevance ranking for system logs at Paranal Observatory, and sensor based degradation modeling in bottling lines, highlighting our interdisciplinary approach to operations research, machine learning, and decision support.

13th International Conference on Mathematical Methods in Reliability

Our team participated in MMR 2025 with three talks showcasing recent advances in data-driven maintenance. The presentations covered applications in the airline industry, distributed systems, and the wine sector, highlighting our interdisciplinary approach to predictive modeling, anomaly detection, and operational optimization.