Research Interest

My research interests center around the intersection of machine learning and computer vision for safe autonomous driving, with a focus on scene understanding and the potential of large language models (LLMs) and large vision-language models (LVLMs) for autonomous driving. My prior work on Driving Behavior Analysis includes developing end-to-end deep learning and inverse reinforcement learning-based models for multimodal, context-aware, and interaction-aware motion forecasting. Additionally, I have extracted efficient driver embeddings from driving time series using scalable representation learning techniques and leveraged graph-based spatiotemporal convolutions to model interactive behavioral patterns among multiple agents within driving scenes.