Developing intelligent inference by treating topological patterns in graphs and networks as constraints on a random graph ensemble.→
Capturing in simulations and mathematical form the surface structure of crystals and how they coalesce when heated but not melted.→
Applying spectral-like theories to the bipartite network of products and capabilities to find latent potential in countries and firms.→
Demonstrating collective creativity: anonymous collaboration under constrained freedom that transcends the creativity of the individual.→
Reconstructing the 3d shape distribution of grains or other objects randomly packed together with access only to 2d slices through them.→
Developing new local and global measures for networks derived from social interactions to infer social structure, sentiment and behaviour.→
Simulating the molecular structure of materials under pressures so extreme that we are not yet able to study them in the laboratory.→
Extending the theory of thermodynamics to account for quantum mechanics and the optimisation of energy transfer in quantum systems.→
Developing radical new approaches to inference and automated decision making using advances in quantum information and statistical physics.→
Understanding the dynamics of memristor networks, a new approach to low-power computation inspired by the structure of the brain.→
Taming limitations of general relativity, such as the big bang singularity, by formulating theories that admit bouncing or cyclic universes.→
Creating discrete models of space that obey Euclid’s axioms over long distances and open up new possibilities for non-continuum physics. →
Developing a statistical physics model of recursive innovation in which technologies become the building blocks for new technologies.→
Advancing the mathematical theory of bootstrap percolation, where active cells on a lattice with few active neighbours cease to be active.→
Developing a mathematical theory of how trust trees grow and how we can traverse them to exploit trust corridors in society for searching.→
Examining the effect of public opinion on stock market returns and harnessing social sentiment to make quantitative market predictions.→
Creating mathematical models of combinatorial innovation to understand how innovation rates can be influenced as components are acquired.→
Creating powerful mathematical methods for predicting cancer outcomes that can be coded in algorithms for fast parallel processing.→
Designing optimal self-similar structures for compact counter-current heat exchangers to reduce heating costs and greenhouse emissions. →
Investigating the adverse effects of information asymmetry and deliberate errors in social media and the press and attempts to remedy them.→
Predicting the geometry of densely packed objects in the natural and man-made worlds, from rocks to foams to polydisperse spheres.→
Exploring the properties of hypercube and Hamming graphs and their subgraphs for insights into coding theory and models of evolution.→
Using ideas from statistical physics to reconstruct the average properties of financial networks from partial sets of information.→
Developing a new approach to resilience in which mistakes and unexpected events are mitigated by easy repairs rather than redundancy.→
Understanding complex dynamical behaviours generated by simple rules, such as cellular automata, polyominoes and models of aggregation.→
Forecasting the rate of technological progress by harnessing empirical regularities captured by Moore’s law and Wright’s law.→
Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.→
Applying ideas from diversification and cascading failures to mitigate the propagation of risk across inter-connected institutions.→
Using fractal, or self-similar, patterns to design the lightest possible load-bearing structures with new strength-to-mass scaling laws.→