The Concentration of measure phenomenon is a fundamental tool of high dimensional probability and of Asymptotic Geometric Analysis. Independence or Isoperimetry
are two typical reasons for the appearance of this phenomenon. In these talks I will introduce
the phenomenon and I will show how High dimensional Geometry affects the concentration.
In particular I will explain how “convexity” can be used to establish strong concentration inequalities in the Gauss space and how the “convexity” of the underline measure is responsible
for deviation principles.