My research (plain descriptions)

I work across a range of areas involving applying quantiative modeling to real-world systems, using mechanistic models (as a physics-based approach) as well as statistical models (e.g., using methods from machine learning). I've worked in sleep modeling, material hardness simulations, time-series analysis, machine learning, and network neuroscience.
I am currently working on problems in both time-series analysis and in computational neuroscience, with the aim of discovering general principles of brain organization using a combination of statistical and physical modeling. I am based in the Complex Systems group in the School of Physics at Sydney University.
If you're interested in working on time-series analysis, machine learning, or developing and applying new quantitative methods to understand the brain, I have funding available for PhD students: please throw me an email if interested.
Click the button below if you're interested in reading my attempts to explain some of my research papers in clear, non-technical terms.

Highly comparative time-series analysis

We developed an interactive website, CompEngine, that allows you to upload and explore connections between your time-series data and a library of thousands of diverse empirical and synthetic time series. Have a play, it's fun!


I have also developed a software package, hctsa, for applying thousands of time-series analysis methods to a time-series dataset. The analysis code is available below:

Get the code (non-commercial) Request license from Engine Analytics (commercial)


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