My primary research interest lies in the application of mathematics to develop AI algorithms that perform consistently across varied data distributions. My focus areas within deep learning include:

• Representation Learning
• Semi-supervised, self-supervised, and unsupervised methods
• General purpose models supporting multilinguality and multimodality
• Graph Neural Networks (GNNs)
• GNN applications in knowledge graphs, drug discovery, social networks, reasoning, and computational biology
• Impact of model scaling (e.g foundational models) on downstream tasks
• Intersections of machine learning and neuroscience
• Interpretation of black box models