Camile Lendering

I am a PhD candidate at Eindhoven University of Technology (TU/e), working in the AIMS laboratory (AI for Multi-modal Sensing). My research focuses on anomaly detection in visual data for monitoring and inspection tasks.

Anomaly detection is challenging in settings where failures are rare, system behavior is complex, and manual labeling is limited. In my work, I study how irregularities in visual and structural data can be identified reliably under such conditions, with attention to robustness, scalability, and interpretability.

My research is motivated by industrial applications, including the monitoring of large-scale engineered systems such as wind energy infrastructure, while addressing challenges that are relevant across different application domains and sensing modalities. I collaborate with academic and industrial partners to develop methods that can be applied in long-term, real-world monitoring scenarios.

I completed the Erasmus Mundus Joint Master’s programme in Artificial Intelligence (EMAI). During the programme, I specialised in Data Science at the Faculty of Computer and Information Science (FRI), University of Ljubljana.

I am currently pursuing a PhD at Eindhoven University of Technology (TU/e), where I conduct research within the AIMS laboratory (AI for Multi-modal Sensing). My doctoral work focuses on anomaly detection for monitoring and inspection problems, with an emphasis on applied research in industrial settings.