Talks, Lectures, and Presentations

Synchronisation and SelfOrganisation under the Free Energy Principle
Description
Abstract:
We will trace through the logic of (a particular form of) Friston’s free energy principle and show how one arrives at the statement “interacting random processes with nonequilibrium steady state densities look as if they minimise variational free energy.” We will then explore how the FEP can be related...

A Physical Account of the Free Energy Principle
Description
Abstract:
We will trace through the logic of (a particular form of) Friston’s free energy principle and show how one arrives at the statement “interacting random processes with nonequilibrium steady state densities look as if they minimise variational free energy.” We will then explore how the FEP can be related...

Approximate Bayesian Inference Through the Lens of Large Deviations
Description
Abstract:
I will discuss an approach to approximate Bayesian inference rooted in stochastic analysis, specifically, using large deviations theory. I will first summarise what a large deviations principle is and how they appear to be fundamental in probability theory and statistical physics. I will then use pathwise large deviations to...

PathWise Large Deviations Theory and Some of Its Applications
Description
I gave the following talk at the Hunter College Mathematics Colloquium. Many thanks to Vincent R Martinez for inviting me.
Abstract:
Since the work of Ellis in the eighties, we have known that large deviations theory gives us a natural way of making the sorts of asymptotic statements about probability...

Large Deviations and Statistical Inference in Phased Materials
Description
I gave a talk at the AMS Western Sectional in the following special session: PDEs, Data, and Inverse Problems. The meeting was hosted by the University of Utah. Many thanks to Vincent R Martinez for inviting me.
Abstract:
Physical systems and the partial differential equations used to...

Classical Physics for the Bayesian Mechanic
Description
This is a poster about a paper of mine, A Worked Example of the Bayesian Mechanics of Classical Objects. It will appear at IWAI2022, in conjunction with ECML PKDD 2022. Interested readers can also see me do a ~one minute virtual pitch of the poster. I will...

A Training Manual for Bayesian Mechanics
Description
I went to see the University of Strathclyde’s Mathematically Structured Programming Group to speak about Bayesian mechanics and its possible relations to categorical cybernetics.
Abstract:
Bayesian mechanics is a new set of tools for studying the mathematical physics of coupled random dynamical systems. In this talk I will provide...

Statistical Inference and the Parallel Transport of Probability
Description
I gave the following talk at the Union College Mathematics Conference, in the Stochastic Analysis and Applications track.
Abstract:
Methods in statistics like maximum entropy usually focus on the probability measure associated to a dynamical system or field theory with probabilistic degrees of freedom; in so doing, statistical inference yields...

Morphogenesis : Basal Cognition :: SelfOrganisation : Maximum Entropy
Description
I gave a talk to the Levin lab about my recent work on gauge symmetries, inference, and selforganisation.
Abstract:
We give an account of inference in the context of selforganisation, with particular focus on structures like Turing patterns and developing cell lines—objects which seem too simple to be cognitive,...

Towards a Geometry and Analysis for Bayesian Mechanics
Description
I returned to UCL’s Theoretical Neurobiology seminar to speak about my recent work on gauge symmetries in inference.
Abstract:
This talk gives some geometric intuitions for a functionalanalytic and gaugetheoretic rewriting of variational inference in the free energy principle. We will sketch out a proof, at a mostly conceptual...

PDE Analysis via Inference: Treating Dynamical Systems as DataGenerating Processes
Description
I gave the following talk at TTU’s Learning from Data seminar.
Abstract:
Due to their variety and potential complexity, a general treatment of the theory of partial differential equations is an open problem in analysis and mathematical physics. However, despite the theoretical challenges posed by finding, and solving, the equations...

A Higher Gauge Theory for Machine Learning and Inference
Description
I submitted this account of some preliminary work to a poster session for ECRs at my institution.
Abstract:
We consider the mathematical theory underlying deep learning and machine learning, and attempt to motivate the success of such algorithms in finding solutions to dynamical problems. We use Jaynes’ Maximum Calibre, a...

Creating Personalised Neuromedicine Using Artificial Intelligence and Brain Modelling
Description
This was a short keynote I gave about the application of multiscale modelling to datadriven neurology and psychiatry. A recording is available upon request.
Abstract:
Today, the utility of data in medicine is rapidly increasing, due to increased precision and availability. To maximise the impact this has, clinicians and researchers...

Constructing Solutions to Arbitrary Diffusion Processes Using Higher Geometry and Maximum Calibre
Description
I gave the following seminar at UCL’s Theoretical Neurobiology meeting.
Abstract:
How can we construct solutions to arbitrary diffusion processes? This question poses a challenge, as diffusion processes can be characterised as (nonlinear, chaotic) partial differential equations. As such, this is effectively a subset of a large, open problem...

The Cortex in the Code: How Neuroscience Makes AI Intelligent
Description
Abstract:
Today’s AI algorithms closely parallel the function of the brain, such that they are able to do many tasks as well as, or better than, humans. This talk will examine not only the functional parallels between the brain and AI but also the structural parallels, drawing comparisons between AI...