Social and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty. However, real-world interventions are almost always plagued by limited resources and limited data, which creates a computational challenge: how can we use algorithmic techniques to enhance the targeting and delivery of soci...

We study the problem of welfare maximization in a novel setting motivated by the standard stochastic two-stage optimization with recourse model. We identify and address algorithmic and game-theoretic challenges that arise from this framework. In contrast, prior work in algorithmic mechanism design has focused almost exclusively on optimization problems without uncertainty. We make two kinds of ...

This thesis introduces analysis tools for improving the current state of the art of heuristics for the Maximum Clique (MC) problem. The analysis focusses on algorithmic building blocks, on their contribution in solving hard instances of the MC problem, and on the development of new tools for the visualisation of search landscapes. As a result of the analysis on the algorithmic building blocks, ...

Algorithmic problem solving provides a radically newway of approaching and solving problems in general by using the advances that have been made in the basic principles of correct-by-construction algorithm design. The aim of this thesis is to provide educational material that shows how these advances can be used to support the teaching of mathematics and computing. We rewrite material on elemen...

Abstract This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, argues is not moral concern except when special circumstances apply. However, same algorithm or different algorithms based on data are used multiple contexts, a person may...