Bhaskar Ray Chaudhury
University of Illinois Urbana-Champaign, IL·Computer Science·Early-career
How should a group share things fairly when the things being shared are unusual: unpleasant tasks that nobody wants, items that cannot be split, or data that can be copied for free? Questions of fair division and fair trade are old ones in economics.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
fair division
fair allocation
chore allocation
- Grants
- 1 known grant, through Dec 2029
What they work on
- 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.
- Resolve open questions about whether fair allocations of indivisible goods always exist, by developing discrete fixed-point theory, topology and algorithms for settings where continuous fixed-point theorems fail.
- Establish axiomatic foundations for sharing and trading data in federated learning and data marketplaces, where data can be copied freely and its value is uncertain.
- Determine the feasibility and limits of achieving fairness when data is shared or exchanged among machine-learning participants.
More about the research
Online labor markets, home-sharing platforms, peer-review systems and machine-learning data marketplaces now raise new versions of them, and existing mathematical tools often break down on these. This CAREER project aims to build the theory that these new economies need. Along the way it expects to advance optimization, algorithm design and fixed-point theory, the branch of mathematics that proves stable solutions exist.
Why it matters
Gig-work platforms, ridesharing and homestay apps, peer-review systems and data marketplaces all decide who gets what. Stronger theory and algorithms for fair allocation could help designers of these systems divide tasks, items and data more fairly among workers, users and organizations. They could also clarify when fairness guarantees are impossible in collaborative machine learning.
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.
Christian Kroer
Columbia University, NY·Computer Science·Early-career
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.
Simina Branzei
Purdue University, IN·Computer Science·Early-career
Analyze market dynamics in which computationally limited players with only local information interact over time, testing whether globally efficient allocations emerge and whether markets grow or shrink.
The school
University of Illinois Urbana-Champaign: 41 labs on PI Pages, $907M a year on research, 870 research doctorates a year.
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