I lead the Dynamics and Neural Systems Group in the School of Physics at Sydney University. The University of Sydney has a beautiful campus full of intelligent people. It also has a vibrant interdisciplinary community that includes researchers in physics, maths, statistics, computer science, engineering, psychology, physiology, and biomedical science, all investigating the brain.
Our group develops a lot of open-source software for time-series analysis, including hctsa, pyhctsa, catch22, and pyspi. A summary of the time-series analysis software we've developed and maintain is here.
If you're interested in working with me as a PhD student or postdoc, see the join page on the group website. I have some top-up funding available for strong PhD students.
Through Engine Analytics, I work with industry and research teams on applied time-series analysis problems. We do consulting and deliver tailored software in diverse domains, from biomedicine to engineering.
Get in touch if you have a time-series problem and are looking for advice.
I have a diverse and interdisciplinary training in physics, dynamical systems, brain modeling, and statistical learning. My research interests are correspondingly diverse, but can be broken up into two main themes:
The modern world is drowning in data. Statistical approaches can find patterns hidden in these data, but how can we understand how these patterns come to be? This requires a combination of skills in statistical analysis and physical modeling. We use a mix of techniques from statistical physics to machine learning to understand diverse systems, from earthquakes to heart rates, share prices to climate. If we find informative patterns hidden in abnormal sleep recordings, pathological heart rhythms, or audio measured in Parkinson's disease, we might be able to do some serious good.
My research in Complex Systems uses techniques from statistical physics, information theory, and machine learning to extract and understand patterns emerging from big datasets of complex real-world systems.
The brain is the most complex physical system we know of and its intimidating complexity forms the physical basis for our thoughts and feelings. The brain's organization, and the mechanisms through which it allows us to make sense of the world around us, are ultimately physical mechanisms that have been selected for by evolution. From the spatial patterning of the brain’s structural properties, to the role of cortical waves in transmitting information, evolution has exploited a diverse physical processes to enable efficient learning. Physicists can distill modern neuroscience datasets down to interpretable principles and predictive theories about how the brain works, which may help us to learn from how the brain processes information to better treat brain disease and build new intelligent machines.
Neuroscience is rich in data, but poor in theory. We have an extraordinary descriptive understanding of the brain: we know how many of its parts are structured, and how they work, and we can measure the brain’s structure and dynamics in unprecedented detail. But when it comes to the type of understanding that would allow us to make deeper statements about how the brain works, we are more or less at square one. This makes neurophysics a very exciting area to work in!
My research in neurophysics finds and explains patterns in the brain using methods from statistics and physics, with the ultimate aim of understanding principles of brain organization and function in terms of physical mechanisms of information processing.
I played cello/piano in a Melbourne band called patches. Take a listen?
Bandcamp Spotify Apple Music/iTunes Music video for our single, White Noise