I work on representation learning and model evaluation, using internal representations as a practical lens into deep networks’ behavior.
My area of Research is in Continual Learning, especially in the domain of Generalized Category Discovery.
My PhD research explores schema-free entity relationship graphs for named entity recognition using large language models. The objective is to build agentic LLM-based systems that generalize beyond fixed ontologies, improving adaptability and performance across diverse and unseen domains.
I research end-to-end and foundational models for autonomous driving, with a strong interest in computer vision and robotics to enhance perception, decision-making, and control in dynamic environments.
Primary research interest includes test and evaluation methodologies for neural networks with a specific focus of mechanistic interpretability for multimodal models.