If you like modern AI with latent diffusion + flow matching, take a look at [1] well before latent diffusion, you will see how natural variation can arise naturally from statistical laws built with PDEs, continuum mechanics, + Bayesian priors that arise from these operators+their Green's functions.

This later led to metric pattern theory, a general framework to understand variation in objects, a general theory of metrics on diffeomorphisms, and procedures to construct flows that do not fold (diffeomorphisms) by integrating velocity fields.

..the deformations u(x) result from a stochastic differential equation Lu = e, where L is a self-adjoint differential operator, whose covariance can be learned from data, and may be non-stationary.

But work by Michael Miller, Ulf Grenander, and the Brown Pattern Theory school showed that natural variation in brain geometry, and function, could be modelled as a set of probabilistic transformations of a template, where ..

In the 1990s, as statistical parametric mapping was being developed, the standard way to study disease effects on the brain was to average images together.

Hurray, many congrats !! :) 🎉

Haha exactly :)

Haha I bet the film crew decided they had to go to Switzerland :)

I hadn’t see these Alex and now I am going to be hooked on them!! I miss all these places but somehow only go visit for about a day at a time - more great places for the list thank you 🙏

Haha you have an amazing memory !! Hoping you are warmer South of the Watford Gap :)

Haha you have an amazing memory !! Hoping you are warmer South of the Watford Gap :)