Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Statistical methods that normally fail for very high-dimensional data can be rescued via mathematical tools from statistical physics.
We optimize Bayesian data clustering by mapping the problem to the statistical physics of a gas and calculating the lowest entropy state.
An explicit analytical solution reproduces the main features of random graph ensembles with many short cycles under strict degree constraints.
Exact equations for the thermodynamic quantities of lattices made of d-dimensional hypercubes are obtainable with the Bethe-Peierls approach.
Entropies of tailored random graph ensembles: bipartite graphs, generalized degrees, and node neighbourhoods
Ensembles of tailored random graphs allow us to reason quantitatively about the complexity of system.
A review of the achievements concerning typical bipartite entanglement for random quantum states involving a large number of particles.
The immune system must simultaneously recall multiple defense strategies because many antigens can attack the host at the same time.
An intriguing analogy exists between neural networks and immune networks.
New mathematical tools quantify the topological structure of large directed networks which describe how genes interact within a cell.