I am a PhD scholar in the Department of Data Science, with research interests in time-series analysis, anomaly detection, deep learning, and Explainable AI. My research focuses on developing reliable and interpretable learning methods for complex multivariate time-series data with applications in industrial and real-world systems.

My research has particularly focused on time-series anomaly detection, which is an important requirement for the preventive maintenance of many real-world critical systems. My broader research goal is to develop robust and explainable AI methods that combine technological advances in machine learning with practical solutions to challenging, real-world problems.