TRC3500 - Sensors and Artificial Perception
ECE4087 - Medical Technology Innovation

Dr Elizabeth Zavitz is an interdisciplinary computational neuroscientist whose research group at Monash University investigates biologically inspired algorithms for visual perception, employing computational modelling, physiological recordings and behavioural analysis to study how the brain encodes and transforms sensory information. Her work focuses on understanding how distributed neural populations represent complex visual patterns, such as texture and natural images and how these representations contribute to perceptual and behavioural outcomes.

Dr Zavitz completed a Bachelors Degree in Computing (2007) and a PhD in Experimental Psychology (2013) in Canada. Following her doctoral studies, she undertook postdoctoral research in the physiology of neural information processing in Australia before establishing her independent research program at Monash University. Her interdisciplinary trajectory reflects an enduring commitment to linking the mechanisms of biological vision with the design of artificial perceptual systems.

Her laboratory integrates computational and empirical approaches to explain the principles that govern visual coding in the brain. This research informs the development of medical technologies and artificial vision systems, enhancing the understanding of how perception arises from distributed computation. Her current project, Building a Visual World: How the Brain Creates Representations of Visual Information, examines how experience and context modulate neural responses. Dr Zavitz’s academic leadership promotes cross-disciplinary collaboration, combining theoretical precision with methodological innovation to advance computational neuroscience.

Dr Zavitz’s research examines the neural and computational foundations of visual perception. Her work integrates computing, psychology and physiology to identify the principles that govern information representation in the brain. Through computational modelling and empirical investigation, she seeks to explain how distributed neural populations encode and transform visual stimuli into perceptual experience.

Her laboratory at Monash University employs deep learning models, physiological recordings and behavioural experiments to investigate visual processing. This integrated approach enables the identification of algorithms that underpin visual coding and supports the development of new analytical methods for quantifying perceptual complexity. Current projects include the use of artificial neural networks trained on natural textures to understand the organisation of cortical representations and how context influences perception.

Dr Zavitz’s research advances both neuroscience and engineering by connecting biological and artificial systems. The resulting insights inform the design of medical technologies and artificial vision platforms, particularly in the areas of neural decoding and brain–machine interfacing. Her work contributes to a broader understanding of how sensory systems achieve efficient information processing and how those mechanisms can be translated into practical technological innovation.