Research
Four strands, one question: how coherent behaviour arises in a collective that nobody is directing — and what it takes to steer it once it has.
Language-driven control of living systems
Prompt-to-Intervention
Can you tell a cellular collective what to do, in ordinary English, and have it comply?
Prompt-to-Intervention (P2I) is an architecture that maps a free-form sentence onto a bioelectric intervention on a collective of cells. A sentence embedding conditions a multi-head convolutional policy network, whose parameters are found by CMA-ES. The crucial move is the reward: rather than hand-specifying a target pattern, a vision–language model reads the resulting behaviour and scores how well it matches the prompt. Nothing about the target has to be written down in advance.
The system generalises past its training prompts, which is the part that matters. Instructions it has never seen still land on the intended behaviour more often than not — evidence that what is being learned is a mapping between semantic space and dynamical space, not a lookup table.
Artificial life
Evolutionary transitions in individuality
How does a population of individuals become one individual?
VitaNova is a simulation in which agents with evolving neural controllers self-organise into flocks — and then the flocks begin to reproduce as units, dividing to found daughter flocks. Role specialisation appears inside them without being selected for directly.
The point is not that the outcome is surprising; multicellularity and eusociality both happened. The point is that natural selection and self-organisation had to work together to produce it, and the model lets you take the two apart and watch what each contributes.
Computational intelligence
Learning, evolution, and the Baldwin effect
What does an individual’s ability to learn do to the population it belongs to?
My doctoral work concerned evolving self-taught neural networks: agents that generate their own training signal rather than receiving one from the environment. Across dynamic and rugged fitness landscapes, a consistent asymmetry appears — self-teaching buys robustness when the environment moves quickly, while social learning buys speed when it is comparatively stable.
This is the Baldwin effect examined as an engineering question rather than a historical one: not whether it happened, but under what conditions learning is worth its cost to a population.
Dynamical systems for markets
Phase transitions in price
The same transition question, asked of a different collective.
A market reorganises without anyone deciding that it should — which makes it, formally, the same kind of object as the collectives above. I treat it as a dynamical system with identifiable regimes, so that the question stops being “is this reading high or low” and becomes “which regime is this, and is it building or breaking”.
Working this way makes several timeframes resolvable as one object rather than several charts to reconcile, and makes the set of possible turning-point events enumerable instead of interpretive — including the transition that starts and then fails, which is the common case and the one most approaches discard as noise.
This strand is commercial rather than academic: the method is proprietary and unpublished. It is built and running as Theta.