Optimization models typically seek to maximize overall benefit or minimize total cost. Yet
fairness is an important element of many practical decisions, and it is much less obvious …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

The demand for same-day delivery (SDD) has increased rapidly in the last few years and
has particularly boomed during the COVID-19 pandemic. The fast growth is not without its …

We consider the problem of generating rankings that are fair towards both users and item
producers in recommender systems. We address both usual recommendation (eg, of music …

There has been a dramatic growth of shared mobility applications such as ride-sharing, food
delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services …

The ride-hailing service offered by mobility-on-demand platforms, such as Uber and Didi
Chuxing, has greatly facilitated people's traveling and commuting, and become increasingly …

We study the allocative challenges that governmental and nonprofit organizations face when
tasked with equitable and efficient rationing of a social good among agents whose needs …

The ubiquity of digital platforms is undeniable, as is their transactional efficiency and world-
flattening possibilities. Yet, these platforms are contested battlegrounds where stakeholder …

We consider the allocation of limited resources to heterogeneous customers who arrive in
an online fashion. We would like to allocate the resources" fairly", so that no group of …

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned
into clusters that consist of nearby points in a metric space. A recent variant, fair clustering …