You surely didn’t come here to look at my face.
You came here because of one of my projects:
My notebooks concerning whatever shiny thing distracted me and needed notes taken, such as
- interesting problems in statistical inference,
- tedious technicalities in solving said interesting problems,
- survival tips, broadly construed, for wherever I am living, and
- philosophy of each of the above things.
Lotekno, a Sundanese pop/electronic dance music crossover collaboration that I co-founded.
Bodywerk, my slammin’ DJ project.
Synestizer, an audiovisual synthesizer for which I am technical lead.
Efek Samping, my solo abstract electronic art music project.
My github page hosts various software I wrote.
Wait, you did come here for my face?
You’ll have to scroll down a little, then.
Hi, I’m Dan MacKinlay.
I’m a statistician and musician from Australia. Musician should be clear. Statistician, though, what’s that? A statistician is the exact same thing as a data scientist or machine learning researcher with the differences that there are qualifications needed to be a statistician, and that we are snarkier.
These days I work across data analysis, music, generative design and machine learning, especially for time series. I’ve been based at various times at such locations as
- Zürich, Switzerland, where I did my MSc in statistics under Professors Sara van de Geer and Didier Sornette at the Swiss Federal Institute of Technology
- Sydney, Australia, where I visualised data for the Powerhouse Museum under Seb Chan
- Bandung, Indonesia, where I worked on interactive music with Common Room and Gustaff Harriman Iskandar
- University of New South Wales Sydney where I undertook graduate studies with Zdravko Botev.
I’m currently at CSIRO’s Data61 researching topics in hybrid machine learning methods for physical sciences.
My current methods of interest are point process inference, compressive sensing, Gaussian processes, sequential Monte Carlo methods, concatenative synthesis, differentiable learning, branching processes, Hilbert-space methods in high dimensional inference, stochastic differential equations and the application of all of these to physical modeling engineering and sick breakbeats.