Christian Kroer
Columbia University, NY·Computer Science·Early-career
Markets are how societies decide who gets what, and the idea works even when no money changes hands. Medical residents are matched to hospitals, students to schools, kidneys to patients, course seats to undergraduates, families to public housing, and computing time to research groups — all allocation problems.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
market design
market equilibrium
fair division
- Grants
- 1 known grant, through Jun 2028
What they work on
- Develop algorithms for online fair allocation and dynamic markets using online learning and first-order optimization methods, aiming for robustness across different input models rather than a single assumed arrival process.
- Extend optimistic and predictive online learning, which exploits machine-learned forecasts of future inputs, to the fair allocation setting so that predictions improve allocation quality without sacrificing worst-case guarantees.
- Formulate a class of two-sided Fisher market models, in which both sides of a matching market are agents with preferences deserving fair treatment, and design equilibrium computation algorithms for them.
- Define new utility classes capturing combinatorial preferences over bundles of goods and build the associated optimization models and algorithms, since standard market models do not represent such preferences.
- Initiate statistical inference for Fisher markets, testing whether equilibrium quantities from sampled markets are asymptotically normal and developing a theory of counterfactual inference about alternative market designs.
More about the research
Economic theory offers a clean answer in the form of market equilibrium: a set of prices at which supply exactly meets demand, which can be applied to moneyless settings by handing every participant a budget of artificial currency. The obstacle is computation. Turning equilibrium from a theoretical ideal into a working allocation system requires algorithms that can find these prices at realistic scale, and that handle the messiness of real settings. This project develops an optimization and AI-driven approach to large-scale market equilibrium computation.
Why it matters
Allocation decisions that affect people directly — which school a child attends, who receives public housing, how course seats or blood-donation appointments are distributed, what a recommender system shows — are often made by ad hoc rules. Scalable algorithms for computing market equilibria could make such systems both more efficient and demonstrably fairer, and statistical inference tools would let operators attach uncertainty estimates to outcomes and compare alternative designs before deploying them.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Hadi Hosseini
Pennsylvania State University, PA·Computer Science·Early-career
Design matching and allocation algorithms that remain fair when participants' preferences are uncertain or noisy, rather than assuming preferences are reported exactly.
Bhaskar Ray Chaudhury
University of Illinois Urbana-Champaign, IL·Computer Science·Early-career
Develop fair methods for allocating and exchanging chores, tasks that cost the people who receive them, as in online labor markets, using continuous optimization, equilibrium analysis and combinatorics.
Vijay Vazirani
University of California-Irvine, CA·Computer Science
Design algorithms for online hypergraph matching, where each match can involve more than two parties, and its generalizations, building on the simple proof of the RANKING algorithm, with applications to ride-sharing and network revenue management.
The school
Columbia University: 89 labs on PI Pages, $1.4B a year on research, 625 research doctorates a year.
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