Bio
Julie began her education in psychology and cognitive science before developing an interest in computer modelling, focusing on neural network models of visual processing and perception. Her research interests have expanded toward large-scale AI systems, particularly questions surrounding representation, perception, and emergent behaviour in large language models. Julie later accepted a DPhil position in computational neuroscience at the University of Oxford.
She is also deeply interested in the societal, ethical, and policy implications of AI technologies and has pursued this work as well. Advances in artificial intelligence have strengthened Julie’s enthusiasm for using technology to address real-world problems, while also deepening her appreciation for the complexity of the human mind.
research summary
Julie’s research interests span computational neuroscience and artificial intelligence, focusing on how complex representations emerge from distributed systems. Her earlier research examined neural network models of primate vision, invariant object recognition, and colour perception, including biologically inspired spiking neural networks aimed at understanding the “binding problem” in perception. More recently, she is interested in questions concerning representation, structure, and emergent behaviour in AI systems. Her current work explores the normative architecture and representational dynamics of large language models (LLMs).