[AI Seminar] AI Lunch - Feb 9th Room Change

[Ellen Vitercik]

Hello everyone,

This is a reminder that this lunch and talk is tomorrow, Tuesday, February
9th.
Best,
Ellen

On Thu, Feb 4, 2016 at 9:17 AM, Ellen Vitercik <vitercik at cs.cmu.edu> wrote:

> Dear faculty and students,
>
> Please note that AI lunch <http://www.cs.cmu.edu/~aiseminar/> will be in *NSH
> 1507* this Tuesday, February 9th at noon. John Dickerson
> <http://jpdickerson.com/> will speak about Better Matching Markets via
> Data and Optimization: Evidence from a Nationwide Kidney Exchange.
>
> *Abstract*: The exchange of indivisible goods without money addresses a
> variety of constrained economic settings where a medium of exchange—such as
> money—is considered inappropriate. Participants are either matched directly
> with another participant or, in more complex domains, in barter cycles and
> chains with other participants before exchanging their endowed goods. We
> show that techniques from computer science and operations research,
> combined with the recent availability of massive data and inexpensive
> computing, can guide the design of such matching markets and enable the
> markets by running them in the real world.
>
> A key application domain for our work is kidney exchange, an organized
> market where patients with end-stage renal failure swap willing but
> incompatible donors. We present new models that address three fundamental
> dimensions of kidney exchange: (i) uncertainty over the existence of
> possible trades, (ii) balancing efficiency and fairness, and (iii) inherent
> dynamism. For each dimension, we design scalable branch-and-price-based
> integer programming market clearing methods. Next, we combine these
> dimensions, along with high-level human-provided guidance, into a unified
> framework for learning to match in a general dynamic setting. This
> framework, which we coin FutureMatch, takes as input a high-level objective
> (e.g., "maximize graft survival of transplants over time") decided on by
> experts, then automatically learns based on data how to make this objective
> concrete and learns the "means" to accomplish this goal—a task that, in our
> experience, humans handle poorly.
>
> We complement our theoretical models and claims with extensive experiments
> on real data from the United Network for Organ Sharing (UNOS) Kidney Paired
> Donation Pilot Program, a large kidney exchange clearinghouse consisting of
> 60% of the transplant centers in the US. The UNOS exchange uses our
> algorithms and software to autonomously match donors to patients twice per
> week.
>
> Best,
>
> Ellen and Ariel
>
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