ERC Consolidator Grant · 2017
Noise-sensitivity of a Boolean function with iid random input bits means that resampling a tiny proportion of the input makes the output unpredictable. This notion arises naturally in computer science, but perhaps the most striking example comes from statistical physics, in large part due to the PI: the macroscopic geometry of planar percolation is very sensitive to noise. This can be recast in terms of Fourier analysis on the hypercube: a function is noise sensitive iff most of its Fourier weight is on "high energy" eigenfunctions of the random walk operator. We propose to use noise sensitivity ideas in three main directions: (A) Address some outstanding questions in the classical case of…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.