Matthew Kowal is a research resident at FAR.AI working on persuasion capabilities in LLMs. He specializes in interpreting the internal representations of multimodal models.

Matthew is a PhD candidate at York University, Toronto. His thesis focused on designing multi-layer interpretability methods with an emphasis on applications to understanding how spatiotemporal models process concepts across space and time. His previous research focused on interpreting the representations of CNNs with respect to specific concepts, such as measuring shape vs texture and position information.