Nisarg Shah
Associate Professor
Studies computational social choice, fair resource allocation and voting theory.
Nisarg Shah's lab at Toronto works on learning theory & optimization, trustworthy & safe ML and language & society. 29 papers since 2023, 3 at top venues, mostly Artificial Intelligence and Journal of Artificial Intelligence Research. 2 PhD students and 1 master's student. 22 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 10%
- PhD students
- 2
- MS students
- 1
- alumni listed
- 22
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 0
- 7 papers2024 · T1 3
- 8 papers2023 · T1 0
T1 venuesOtherVenues: Artificial Intelligence, Journal of Artificial Intelligence Research, Operations Research
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML10 papers
- allocation2 papers
- Language & society1 paper
- resource allocation1 paper
- Learning theory & optimization0 papers
Paper list
From the lab homepage.
20267 papers
- JAAMASComputational Aspects of Distortion
- EAAMO-26Constrained Fair Allocations via Reductions to Simpler Matroids
- AAAI-26Fairness Perceptions of Large Language Models
- EC-26Proportional Fairness for Harmful Decisions
- Journal of Artificial Intelligence ResearchSurprisingly Popular Voting Recovers Rankings, Surprisingly!
- UAI-26The Art of Calling the Winner by Asking Just Enough Questions
- EC-26The Structure of Bridging
20257 papers
- AAAI-25Constrained Fair and Efficient Allocations
- AAAI-25Every Bit Helps: Achieving the Optimal Distortion with a Few Queries
- AAAI-25Fair Division With Market Values
- EC-25Fairly Stable Two-Sided Matching with Indifferences
- AAAI-25Temporal Fair Division
- AAAI-25The Distortion of Public-Spirited Participatory Budgeting
- NeurIPS-25Unifying Proportional Fairness in Centroid and Non-Centroid Clustering
20247 papers
- Operations Research (T1)Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation
- Artificial Intelligence (T1)Class Fairness in Online Matching
- EAAMO-24Harm Ratio: A Novel and Versatile Fairness Criterion
- TEACOptimized Distortion and Proportional Fairness in Voting
- Artificial Intelligence (T1)Primarily about Primaries
- NeurIPS-24Proportional Fairness in Non-Centroid Clustering
- IJCAI-24What is Best for Students, Numerical Scores or Letter Grades?
20238 papers
- EC-23Best of Both Distortion Worlds
- NeurIPS-23Explainable and Efficient Randomized Voting Rules
- NeurIPS-23Group Fairness in Peer Review
- AAAI-23Partitioning Friends Fairly
- IJCAI-23Proportionally Fair Online Allocation of Public Goods with Predictions
- IJCAI-23Pushing the Limits of Fairness in Algorithmic Decision-Making
- AAMAS-23The Distortion of Approval Voting with Runoff
- AAAI-23Voting with Preference Intensities
Where alumni went22 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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Data
Some numbers could not be verified:
- The lab site and the paper record do not agree.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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