Cardinal objectives serve as intuitive targets in fair machine learning by summarizing utility
(welfare) or disutility (malfare) $ u $ over $ g $ groups. Under standard axioms, all welfare …

“[A] llain Gersten, Hopfen, und Wasser”—1516 Reinheitsgebot We present Bavarian, a
collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) …

We present a novel method for reducing the computational complexity of rigorously
estimating the partition functions of Gibbs (or Boltzmann) distributions, which arise …

We study the problem of approximately counting cliques and near cliques in a graph, where
the access to the graph is only available through crawling its vertices; thus typically seeing …

We present a novel method for reducing the computational complexity of rigorously
estimating the partition functions (normalizing constants) of Gibbs (Boltzmann) distributions …

Introduction My research has always been broadly interdisciplinary, with a focus on
introducing rigorous modern statistical techniques and finite-sample concentration-of …

Here I present a loosely technical overview of the most significant mathematical ideas in my
work, as summarized by three simple inequalities, alongside their broad implications and …