Congratulations to Michel #Talagrand for receiving the 2024 #AbelPrize "for his groundbreaking contributions to probability theory and functional analysis, with outstanding applications in mathematical physics and statistics": https://abelprize.no/article/2024/michel-talagrand-awarded-2024-abel-prize
Michel is perhaps less well known outside of probability than he ought to be. I consider myself a user of probability rather than an expert in the subject, but I have always been impressed by the powerful, deep, general, and non-obvious probabilistic tools that he has developed, particularly his concentration inequality https://en.wikipedia.org/wiki/Talagrand%27s_concentration_inequality (which provides concentration of measure estimates in very general settings, without explicitly requiring otherwise standard assumptions such as Gaussian distribution, martingale structure, or Lipschitz dependence), or his majorizing measures theorem https://projecteuclid.org/journals/annals-of-probability/volume-24/issue-3/Majorizing-measures-the-generic-chaining/10.1214/aop/1065725175.full , that gives a remarkably precise (but highly unintuitive) answer to what the expected size of the supremum of a gaussian process is, in terms of the geometry of that process.