Hadi Hosseini
Pennsylvania State University, PA·Computer Science·Early-career
How should scarce things be divided when money cannot change hands? Seats in public schools, placements for refugees, donated food, shifts in a gig economy, scarce medical equipment during a pandemic — in all of these, selling to the highest bidder is ruled out by law, ethics or practicality, so the allocation must be decided by an algorithm that people will accept as fair.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
fair division
matching markets
resource allocation
- Grants
- 2 known grants, through Sep 2027
What they work on
- Design matching and allocation algorithms that remain fair when participants' preferences are uncertain or noisy, rather than assuming preferences are reported exactly.
- Combine online matching with fair division to obtain fairness and diversity guarantees in dynamic markets where participants arrive and depart over time, as in ridesharing and gig work.
- Blend stable matching with fair division techniques to build two-sided market mechanisms robust to strategic misreporting by participants.
- Characterize the tradeoffs among transparency, fairness and efficiency in allocating indivisible items, and design algorithms that achieve fairness with minimal loss of transparency.
- Identify restricted preference domains in which known impossibility results for transparent fair division can be circumvented, and extend the framework to chores and mixtures of goods and bads.
- Measure empirically how people perceive the fairness of different allocation mechanisms, and release the resulting algorithms in a publicly accessible software platform.
More about the research
Work here sits at the intersection of computer science, economics and artificial intelligence, designing allocation and matching algorithms with provable fairness and efficiency guarantees, and asking what happens to those guarantees under real-world complications: noisy information, participants arriving over time, constraints, strategic manipulation, and demands for transparency.
Why it matters
Decisions about school seats, refugee placement, food donation, ridesharing shifts and scarce medical supplies affect people directly, and unfair or opaque allocation erodes trust — a gap made visible during the Covid-19 pandemic's distribution of medical equipment. Algorithms with provable fairness guarantees that survive noisy preferences, strategic behavior and transparency requirements, released as public software, could let agencies and organizations make such decisions on defensible grounds. Studies of perceived fairness could also show when mathematical fairness matches what people accept.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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.
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.
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
Pennsylvania State University: 60 labs on PI Pages, $1.3B a year on research, 743 research doctorates a year.
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