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 employed at CSIRO’s Data61 in the Machine Learning and Artifical Intelligence Future Science Platform researching topics in hybrid machine learning methods for physical sciences. I’m on the environmental statistics committee of the Statistical Society of Australia.
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.
Want to talk to me? Please do. If you have strong preferences about communication style, you are welcome to let me know them, and might find it useful to look up my preferred communication style. tl;dr I’m argumentative per default, but flexible.
If you have projects that have a budget for data science, statistics or machine learning, AI or whatever they call it this week, let me know. My current contract will not last forever.
If you do not have a budget but believe your cause is righteous, you are welcome to pitch to me for a slice of my volunteer time. Send a short paragraph making a case for what that time will help (I tend to favour climate- and conflict-risk mitigation causes) and why you think it will be a high-leverage use of my time towards that goal.