We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …

We consider un-discounted reinforcement learning (RL) in Markov decision processes
(MDPs) under drifting non-stationarity,\ie, both the reward and state transition distributions …

Abstract Problem Definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi,
we study the problem of matching riders with self-interested drivers over a spatial network …

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 review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …

We consider a fundamental pricing model in which a fixed number of units of a reusable
resource are used to serve customers. Customers arrive to the system according to a …

Ride-hailing platforms, such as Uber, Lyft, and DiDi, coordinate supply and demand by
matching passengers and drivers. The platform has to promptly dispatch drivers when …

We study the matching of jobs to workers in a queue, eg a ridesharing platform dispatching
drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip …

We consider the dynamic pricing problem that arises in the context of an on-demand vehicle
sharing system with one-way trips. Existing results show that a static pricing policy that …

We study a general model on reusable resource allocation under model uncertainty. A
heterogeneous population of customers arrive at the decision maker's (DM's) platform …