She is on the job market this year--you could hire her...
Below (from her web page) is the link to and description of her jobmarket paper.
- Job Market Paper: Kidney Exchange in Dynamic Sparse Heterogenous Pools, with Itai Ashlagi and Patrick Jaillet,Management Science (Revise and Resubmit).
- Extended abstract appeared in The 14th ACM Conference in Electronic Commerce (EC), 2013. [slides in pdf]
- Software: Kidney Exchange Developed primarily by Itai Ashlagi. Please check his homepage for instructions and updates.
- Summary: One of the main challenges in the current practice of kidney exchange is matching highly sensitized patients. These patients are very unlikely to be compatible with a random donor. Current pools of patient-donor pairs are not too large and they contain many such hard-to-match patients. Therefore, the compatibility graphs associated with these pools are sparse (or sometimes called thin). One way to create a thicker pool is to wait for many pairs to join before matching (finding a set of disjoint exchanges), and a major decision clearinghouses are facing is how often to search for allocations. On one hand, waiting is costly; while patients are waiting, they are on dialysis and their health conditions deteriorate. On the other hand, in the current programs, matching too frequently may reduce the number of matched pairs, especially highly sensitized ones.
In this project, we study this intrinsic tradeoff between the waiting time and the number of matches by analyzing a class of algorithms (which are used in practice) that search periodically for allocations. We perform sensitivity analysis on the amount of waiting given different types of allocations (using 2-ways, 3-ways, or chains). We find that if only 2-way cycles are conducted, in order to gain a significant amount of matches over the online scenario (matching each time a new incompatible pair joins the pool) the waiting period should be very long. If 3-way cycles are also allowed we find regimes in which waiting for a short period also increases the number of matches considerably. Finally, a significant increase of matches can be obtained by using even one non-simultaneous chain while still matching in an online fashion. Our theoretical findings (based on random graph models and motivated by clinical data) and computational experiments (using clinical data from some of the largest exchange programs in the US) lead to policy recommendations.