Taha Ameen, "Recent progress on flexibility design in spatial matching markets"

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Abstract: Matching markets pair requests with the resources that can serve them. Such systems are ubiquitous in healthcare, transportation, communication, and computing, where they assign medical residents to hospitals, donated organs to patients, vehicles to riders, workers to tasks, and AI jobs to data centers. Spatial matching markets embed demand and supply nodes in an underlying space, and their proximity in the space determines compatibility. Some service nodes are more flexible than others, i.e compatible with more demand because they have larger service regions. On the other hand, a platform like Uber or TaskRabbit can influence these supply side flexibilities through incentives. How can it do so optimally to optimize downstream performance? We study this question through the lens of probabilistic modeling and bipartite random geometric graphs. Based on joint work with Flore Sentenac and Sophie H. Yu.

Bio: Taha Ameen is a postdoctoral fellow with the computer science department at Carnegie Mellon University. He obtained his PhD in electrical engineering from the University of Illinois Urbana-Champaign. His research interests are in applied probability, operations research, matching markets, and statistical inference on networks. His work has been recognized by several awards including the outstanding poster award at stochastic networks conference, the Jack Wolf best student paper award at IEEE ISIT, and finalist nominations for the INFORMS MSOM student paper award, and the INFORMS APS student paper award.