Here is a description of my research organized by topic area. In addition to papers, you'll find links to several talk videos.

The design of search and matching platforms: In recent years there has been a proliferation of online search and matching platforms, including those for dating, labor and lodging. A core challenge for such platforms is that match formation requires both parties to be mutually interested in each other. Under typical platform designs, participants must spend effort interacting with many potential partners before they can match. Design levers available to online platforms include marketplace rules (for example, a man cannot send the first message on the dating platform Bumble), match recommendations and interest-signaling mechanisms. In a series of papers, we characterize how matching platforms should deploy these design levers so that they produce high quality match outcomes while minimizing demands on participants.


Centralized college and school admissions: School and college admissions constitute an important class of matching markets. I designed and helped implement a new joint seat allocation process for undergraduate admissions to over 500 programs spread across 80 technical universities in India, including the prestigious Indian Institutes of Technology (IITs). The process has been in successful use since 2015 handling over 1 million candidates a year, and has produced a significant reduction in vacancies.

In another paper (Irene Lo's job market paper), we design mechanisms to address the practically important challenge of systematically reassigning seats that are vacated by students at a late stage in the admissions process.


Equilibria in matching markets: In two papers, we characterize equilibria (stable matchings) in matching markets without and with transfers, and show that equilibria are approximately unique. The JPE paper which has received widespread attention. A new working paper revisits random matching markets and explains why real world markets do not exhibit a stark effect of competition.


Algorithms for dynamic matching: Matching platforms must dynamically manage and balance the "inventory" of agents on the two sides of the market, while grappling with agent heterogeneity and noisy predictions regarding future arrivals.

My latest work: Y. Kanoria, “Dynamic Spatial Matching,” working paper. (talk video)

Shared transportation (e.g., ridehailing) systems: This is a "simple" setting where agents and supply units are (primarily) distinguished by geographical location. Even so, prior state-of-the-art policies required perfect knowledge of future demand arrival rates and were impractical. We introduce simple and practical state-dependent control policies that make decisions based on the current geographical distribution of supply, and show them to be near optimal, even without demand predictions. Our first paper studies a setting with "small" geographical imbalance and obtains a sharp large deviations characterization of the (vanishing) optimality gap.

In our second paper, we consider a very general setting and provide a highly practical and near optimal "mirror backpressure" control methodology for joint entry/pricing/dispatch control in the absence of demand predictions. Mirror backpressure autocorrects supply imbalances by aggressively protecting and replenishing cars where they are scarce, while maximally deploying cars from regions where they are plentiful. Notably, mirror backpressure provides a powerful generalization of the celebrated backpressure policy for control of queuing networks.

In a prior paper (Vijay Kamble's job market paper), we introduce and analyze a novel model combining matching and learning.

In an early paper, we study dynamic matching with i.i.d. random agent compatibilities and show near optimality of greedy matching for (i) two-way cycles (matching), (ii) three-way cycles and (iii) chains. Cases (ii) and (iii) are important in kidney exchange, and are also the technically challenging cases. The matching setting (case (i)), is related to the concurrent paper of Akbarpour, Li & Oveis Gharan (for their setting where agent departures occur without warning): in comparison to that paper, our model avoids a nuisance parameter and our result is sharper.


Other recent research: Internet publishers earn revenue by selling the opportunity to advertise to their viewers via auctions. Publishers may try to use past advertiser bids to learn their willingness to pay and set auction reserve prices accordingly, but this approach gives advertisers the incentive to deflate their bids, which hurts revenues. We provide a novel approach for publishers that leverages competition between advertisers to learn their willingness to pay without compromising long-run advertiser incentives.

Customer churn is a significant challenge for service platforms, and is typically managed using discounts and promotions. We propose a novel alternative control lever, namely, how a platform should choose between risky and safe customer service modes to maximize customer lifetime value. Notably, our (optimal) policy results in vastly larger customer lifetime value than a service policy which ignores customer churn (the latter reflects current practice). Some exciting research directions have emerged from my Fall 2019 PhD class on Statistical Physics, Markets and Algorithms:
  • Y. Kanoria, J. Gan, and X. Zhang, “Local algorithms for dynamic optimization in networks,” work in progress.

  • N. Immorlica, Y. Kanoria, and J. Lu, “When does competition and costly information acquisition lead to a deadlock?” work in progress.


Earlier journal papers:


Other Refereed Conference Papers: