Establishing the scientific principles behind the technologies of the future, in order to transform work, health, defence and creativity.
How do we reduce toil, improve health, boost security and enhance creativity? The technologies that address these fundamental human needs always rely on scientific principles, and we develop the theories behind the technologies of tomorrow.
The greatest labour-saving innovation today is efficient computation. We work on theoretical aspects of alternatives to electronic computing, such as memristors and photonic and quantum computing. We seek a deeper foundation for artificial neural computation, which currently resembles engineering more than science.
Can a deeper understanding of how life processes information set the stage for a biological analogue of the silicon revolution? We study whether aging is the result of a programme, rather than an entropic necessity. If so, how can we slow it? We develop new mathematics for high dimensional inference and apply it to precision medicine.
Working with the US Department of Defense and the Ministry of Defence, we lay the theoretical groundwork for moonshot technologies of national interest. These range from self-similar materials, to sensor dust, to repairable instead of robust, to harvesting energy from fluctuations in the environment.
In our theme on the theory of human enterprise, we investigate how we can surpass the limitations of the individual through collective creativity.
Capturing in simulations and mathematical form the surface structure of crystals and how they coalesce when heated but not melted.
Simulating the molecular structure of materials under pressures so extreme that we are not yet able to study them in the laboratory.
Designing optimal self-similar structures for compact counter-current heat exchange to reduce heating costs and greenhouse emissions.
Using fractal, or self-similar, patterns to design the lightest possible load-bearing structures with new strength-to-mass scaling laws.
Developing a theory of high-dimensional statistical inference using analytic tools from the statistical physics of disordered systems.
Reconstructing the 3D shape distribution of rock grains or other randomly packed objects with access to only a 2D slice through them.
Creating powerful mathematical methods for predicting the outcomes of diseases that pinpoint the right treatments and speed up drug trials.
Predicting the geometry and behaviour of densely packed objects from first principles, from spheres to polydisperse spheres to cells.
Understanding the dynamics of networks of memristors, a new paradigm for low-power computation inspired by the structure of the brain.