Building machine learning models that mimic the behaviour of cells in silico to improve the prediction of genes for cell programming.
Deriving the mortality equation, which governs the dynamics of an ageing population, and solving it to crack the evolutionary origin of ageing.
Developing the mathematical structure of experiments using information theory and combinatorics to speed up the discovery of new cell types.
Understanding genetic computation using regulatory motifs, a new kind of structural and functional building block of gene regulatory networks.
Using machine learning to search the vast space of 10-dimensional geometries for ones that predict the Standard Model from string theory.
Generalizing the divisor function to find a new kind of number that can be recursively divided into parts, for use in design and technology.
Developing radical new approaches to inference and automated decision making using advances in quantum information and statistical physics.
Creating a mathematical model of combinatorial innovation to understand how innovation rates can be influenced as components are acquired.
Advancing the mathematical theory of bootstrap percolation, where active cells on a lattice with few active neighbours cease to be active.
Understanding complex dynamical behaviours generated by simple rules, such as cellular automata, polyominoes and models of competition.
Using fractal, or self-similar, patterns to design the lightest possible load-bearing structures with new strength-to-mass scaling laws.
Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.
Using ideas from statistical physics to reconstruct the average properties of financial networks from partial sets of information.
Understanding the physical nature of information and how it relates to energy transfer and new technologies that make use of these insights.
Exploring the spectral properties of subgraphs of the hypercube and Hamming graphs for insights into coding theory and models of evolution.
Predicting the geometry and behaviour of densely packed objects from first principles, from spheres to polydisperse spheres to cells.
Creating discrete models of space and spacetime that appear continuous over long lengths and set the stage for non-continuum physics.
Understanding the dynamics of networks of memristors, a new paradigm for low-power computation inspired by the structure of the brain.
Predicting the behaviour of graphs and processes on them by treating topological patterns as constraints on a random graph ensemble.