Katie Davey

Dr. Katie (Catherine) Davey is an Associate Professor in the Biomedical Engineering Department, in the School of Chemical and Biomedical Engineering at the University of Melbourne. Katie's primary research area is in neuroimaging, in which images of the brain structure and function are acquired using various modalities such as PET, MRI, fNIRS, EEG and DSA. Katie and her team work on advanced techniques to improve image acquisition, reconstruction and analysis, applying stochastic signal processing techniques, in conjunction with artificial intelligence (AI) and machine learning (ML) techniques, to acquire better images (higher SNR, improved spatial resolution) in reduced scan times, increasing patient throughput and the diagnostic utility of the images. Katie also leads a research team applying AI/ML methods to surgery, in which a video feed acquired during surgery is processed to determine how surgical decision making and patient anatomy impact long term patient outcomes. Her team have developed advanced AI/ML algorithms for active learning and self-supervised learning to translate surgical footage into meaningful measures of operative workflow and performance through capabilities such as phase recognition, instrument tracking and the analysis of surgical technique. By linking events captured in surgical video with clinical outcomes, her work aims to support surgical education, quality improvement, collaborative research and evidence-based improvements in patient care. Finally, Katie actively leads research in computational neuroengineering, modelling the evolution of neural networks to support learning via plasticity. Katie's research uses advanced signal processing methods, in conjunction with simulation and modelling techniques, to mathematically and programmatically model cortical processes and gain insight into how we perceive and process sensory information, and how disease processes, such as Alzheimer's, impact these processes. Katie completed her doctoral research in functional MRI connectivity, which is a field of research that analyses a series of low resolution MRI images to identify how brain regions cooperate to achieve sensory and perception tasks. After completing her Ph.D. Katie worked at the Defence Science Technology Organisation, modelling pilot cognition and aircraft control. She then worked in finance, modelling and predicting the movement of stock prices on the S&P500, prior to gaining a continuing position at the University of Melbourne, where she is now an Associate Professor in Biomedical Engineering. Katie is available for student supervision, including honours, Masters, and Doctoral students. Projects will be in brain imaging, AI in surgery, or neural engineering, and will involve both numerical computation and mathematical modelling. Such projects are suited to students with an interest in mathematics and programming. Please note that requests will only be considered and responded to where students have an appropriate background in engineering or similar. Synaptic plasticity and learning, Functional MRI, Machine learning, Signal processing, bioengineering, biomedical imaging, neurosciences

Correction of connectivity induced by autocorrelation and filtering in resting state functional near-infrared spectroscopy data - Journal article, 2026, Journal of Neuroscience Methods Standardized Effect Measures Informing Next-Generation Strategies for Mechanical Stimulation in Cartilage Tissue Engineering - Journal article, 2026, Advanced Healthcare Materials Design-Based Research in Higher Education: Analysing Examples Through Activity Theory and Bakker’s Five Characteristics - Journal article, 2026, Journal of University Teaching and Learning Practice Three-dimensional quantitative morphometric analysis (QMA) allows for reproducible and sensitive assessment of bone and joint in a preclinical mouse model of osteoarthritis - Journal article, 2026, Jbmr Plus Revealing early subchondral b