Welcome!

I am a doctoral researcher in the dtai research group at KU Leuven, under the supervision of Mathias Verbeke and Wannes Meert.

My research focuses on context-aware time series anomaly detection, where I aim to automatically retrieve the important contextual information from the data and leverage it to improve anomaly detection performance. I also study the behavior and performance of existing anomaly detection algorithms under varying conditions to see when and why they succeed or fail. Besides research in anomaly detection, I also focus on learning interpretable representations from time series data which are applicable in various time series analysis tasks, including but not limited to anomaly detection.

My research interests are:

• Time series analytics, including anomaly detection, motif discovery, semantic segmentation, clustering and classification.
• Learning patterns and meaningful representations from time series.
• Explainable AI for time series, with a focus on inherently interpretable models rather than post-hoc explanations.

I am always open to new collaborations and exploring new ideas. Feel free to reach out if you are interested in working together on any of the above topics, or if you see any potential for collaborations in other areas. My expertise lies in time series analysis, but I am always eager to discuss new challenges.