The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

The combination of the compactness of networks, featuring small diameters, and their
complex architectures results in a variety of critical effects dramatically different from those in …

Clustering data by identifying a subset of representative examples is important for
processing sensory signals and detecting patterns in data. Such “exemplars” can be found …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

This is an extended version of my Nobel Lecture, delivered on December 8, 2021. I will
recall the genesis of the concept of multiple equilibria in natural sciences. I will then describe …

A concise and lucid exposition of the major topics in knowledge representation, from two of
the leading authorities in the field.-Stuart Russell, UC Berkeley The information is valuable …

Understanding the molecular determinants of specificity in protein–protein interaction is an
outstanding challenge of postgenome biology. The availability of large protein databases …

Statistical mechanics is our tool for deriving the laws that emerge from complex systems.
Sethna's text distills the subject to be accessible to those in all realms of science and …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

The problem of optimizing over random structures emerges in many areas of science and
engineering, ranging from statistical physics to machine learning and artificial intelligence …