Many patients in need of a kidney transplant have a willing but incompatible (or poorly
matched) living donor. Kidney exchange programs arrange exchanges among such patient …

We consider social resource allocations that deliver an array of scarce supports to a diverse
population. Such allocations pervade social service delivery, such as provision of homeless …

Online matching markets (OMMs) are commonly used in today's world to pair agents from
two parties (whom we will call offline and online agents) for mutual benefit. However, studies …

We study how to optimally match agents in a dynamic matching market with heterogeneous
match cardinalities and values. A network topology determines the feasible matches in the …

We study dynamic matching in exchange markets with easy-and hard-to-match agents. A
greedy policy, which attempts to match agents upon arrival, ignores the positive externality …

Artificial intelligence, machine learning, and algorithmic techniques in general, provide two
crucial abilities with the potential to improve decision-making in the context of allocation of …

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from
ridesharing and online video games to kidney exchange. Such problems are naturally …

We study a generalization of the classical stable matching problem that allows for cardinal
preferences (as opposed to ordinal) and fractional matchings (as opposed to integral). After …

Algorithms for exchange of kidneys is one of the key successful applications in market
design, artificial intelligence, and operations research. Potent immunosuppressant drugs …

We study settings where a set of identical, reusable resources must be allocated in an online
fashion to arriving agents. Each arriving agent is patient and willing to wait for some period …