Our work spans three key areas: 

• Machine learning models on temporal data (sequences and time series) with questions around embeddings, generation of such data and early predictions,
• Adapting machine learning models to evolving data and class distributions over time with questions around domain adaptation, continual learning and sequential learning,
• Trustworthiness of temporal models with questions around explanations, privacy, fairness, robustness and efficiency of such models.