Dan MacKinlay

March 5, 2009 — February 11, 2025

You may have come here looking for one of my other projects, or what I am up to right now.

Or maybe you did come here looking for me? That is sweet of you. Sure, OK. Hi, I’m Dan MacKinlay.

1 Research interests

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, but on average snarkier.

My speciality is the application of statistical inference to AI Safety and the process of scientific discovery, especially in geospatial settings. My methods of interest include Bayesian neural nets, sparse coding, Gaussian processes, sequential Monte Carlo methods, factor graphs, Scientific machine learning… actually quite a lot of stuff.

1.1 Publications

Or check my Google Scholar.

2 Previously

I’ve been based at various times in places like

Minimalist CV here.

3 Contact me

If you have projects with 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 AI, climate and conflict risk mitigation causes) and why you think it will be a high-leverage use of my time towards that goal.

Contact form here.

4 Follow me

Automatic updates in your feed reader:

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You can find me on hypothes.is, Google Scholar, LinkedIn, …

ORCID iD icon orcid.org/0000-0001-6077-2684 .

5 Projects

What I am doing right now.

What I am doing in general:

  • The most obvious project is this very site, concerning whatever shiny thing distracted me and needed notes taken.
  • 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 was technical lead.
  • Efek Samping, my solo abstract electronic art music project.
  • My Github hosts various software I wrote.

6 Values and metrics

By the ClearerThinking.org’s Intrinsic Values Test, my intrinsic values are

  1. That I increase my understanding of reality beyond my current understanding
  2. That humanity does not go extinct
  3. That I feel connected to other people
  4. That I believe true rather than false things
  5. That beautiful things continue to come into existence (e.g. art or music)
  6. That I get to experience a wide variety of different things during my life

7 My face

Thanks to Abdelwahed Khamis for that portrait.

8 References

Ben Rached, MacKinlay, Botev, et al. 2020. A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems.” IEEE Transactions on Wireless Communications.
Botev, MacKinlay, and Chen. 2017. Logarithmically Efficient Estimation of the Tail of the Multivariate Normal Distribution.” In 2017 Winter Simulation Conference (WSC).
Dabrowski, Pagendam, Hilton, et al. 2023. Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires.” Spatial Statistics.
MacKinlay, Dan. 2025. The Ensemble Kalman Update Is an Empirical Matheron Update.”
MacKinlay, Daniel, and Botev. 2019. Mosaic Style Transfer Using Sparse Autocorrelograms.” In Proceedings of the 20th Conference of the International Society for Music Information Retrieval.
MacKinlay, Dan, Pagendam, Kuhnert, et al. 2021. Model Inversion for Spatio-Temporal Processes Using the Fourier Neural Operator.” In.
MacKinlay, Dan, Tsuchida, Pagendam, et al. 2025. Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems.” In Proceedings of the International Conference on Learning Representations (ICLR).
Pagendam, Janardhanan, Dabrowski, et al. 2023. A Log-Additive Neural Model for Spatio-Temporal Prediction of Groundwater Levels.” Spatial Statistics.
Takamoto, Praditia, Leiteritz, et al. 2022. PDEBench: An Extensive Benchmark for Scientific Machine Learning.” In.