Speaker: Dr James Nightingale, Newcastle University 

One of the most profound phenomena exhibited by nature is multiscale complexity, where complex behaviors emerge from the interactions between physical phenomena at vastly different scales of organization.

In Cosmology, an example of a multiscale problem is connecting observations of the Universe’s small-scale dark matter to its large-scale properties immediately after The Big Bang. In Cancer research, understanding how signals at genetic and cellular levels influence national patient healthcare outcomes presents a multiscale dilemma.

I introduce an open-source statistics framework (https://github.com/rhayes777/PyAutoFit) tailored for analyzing multiscale systems. This software integrates Bayesian techniques like hierarchical analysis, expectation propagation, and graphical modeling to construct and fit complex multiscale models to datasets spanning various physical scales. Using examples from Cosmology, I demonstrate the framework's applicability and draw parallels between the challenges faced in modern Cosmology surveys and big-data medical studies.