In this talk, we will revisit the classical regret-minimization problem in the multi-armed bandit setting.

Mitigating the effect of tail risk has gained prominence in a variety of applications where safety is of paramount importance.

Group decision-making is a ubiquitous phenomenon with diverse applications ranging from political elections to recommender systems and from organ exchanges to online marketplaces.

Expander graphs are sparse but highly connected graphs, which find a variety of uses in CS. If the vertices of an expander are labelled by 0 or 1, a $t$-step walk gives a $t$-bit string.