An up-to-date list of my publications can also be found on my Google Scholar page. In most papers listed, authorship follows the convention of alphabetical order of last names.

* indicates undergraduate or high school student at time of research.

Working Papers

  1. Centralized Fairness for Redistricting
    Seyed Esmaeili, Hayley Grape*, and Brian Brubach
    (arxiv version)
  2. Improved Approximation Algorithms for Stochastic-Matching Problems
    Marek Adamczyk, Brian Brubach, Fabrizio Grandoni, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    (arxiv version)

Conference Publications

  1. Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy
    Brian Brubach, Audrey Ballarin*, and Heeba Nazeer*
    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022
  2. Fair Labeled Clustering
    Seyed Esmaeili, Sharmila Duppala, John P. Dickerson, and Brian Brubach
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
  3. Improved guarantees for offline stochastic matching via new ordered contention resolution schemes
    Brian Brubach, Nathaniel Grammel, Will Ma, and Aravind Srinivasan
    Conference on Neural Information Processing Systems (NeurIPS), 2021
  4. Fair clustering under a bounded cost
    Seyed Esmaeili, Brian Brubach, Aravind Srinivasan, and John P. Dickerson
    Conference on Neural Information Processing Systems (NeurIPS), 2021
  5. It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
    Michelle Bao*, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian
    Conference on Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS), 2021
  6. Follow your star: New frameworks for online stochastic matching with known and unknown patience
    Brian Brubach, Nathaniel Grammel, Will Ma, and Aravind Srinivasan
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
  7. Approximating Two-Stage Stochastic Supplier Problems
    Brian Brubach, Nathaniel Grammel, David G Harris, Aravind Srinivasan, Leonidas Tsepenekas, and Anil Vullikanti
    Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2021
  8. Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
    Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, and Leonidas Tsepenekas
    AAAI Conference on Artificial Intelligence (AAAI), 2021
  9. Probabilistic Fair Clustering
    Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas, and John P. Dickerson
    Conference on Neural Information Processing Systems (NeurIPS), 2020
  10. Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives
    Brian Brubach, Aravind Srinivasan, and Shawn Zhao*
    ACM Conference on Economics and Computation (EC), 2020 (conference version)
    Also presented at Harvard CRCS Workshop on AI for Social Good, 2020 (abridged EC 2020 paper)
  11. A Pairwise Fair and Community-preserving Approach to k-Center Clustering
    Brian Brubach, Darshan Chakrabarti*, John P. Dickerson, Samir Khuller, Aravind Srinivasan, and Leonidas Tsepenekas
    International Conference on Machine Learning (ICML), 2020 (conference version)
  12. Fast Matching-based Approximations for Maximum Duo-preservation String Mapping and its Weighted Variant
    Brian Brubach
    Symposium on Combinatorial Pattern Matching (CPM), 2018 (conference version)
  13. A Succinct Four Russians Speedup for Edit Distance Computation and One-against-many Banded Alignment
    Brian Brubach and Jay Ghurye
    Symposium on Combinatorial Pattern Matching (CPM), 2018 (conference version)
  14. Algorithms to Approximate Column-Sparse Packing Programs
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    ACM-SIAM Symposium on Discrete Algorithms (SODA), 2018 (long version)
  15. Better Greedy Sequence Clustering with Fast Banded Alignment
    Brian Brubach, Jay Ghurye, Aravind Srinivasan, and Mihai Pop
    International Workshop on Algorithms in Bioinformatics (WABI), 2017 (conference version)
  16. Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017
  17. Further Improvement in Approximating the Maximum Duo-Preservation String Mapping Problem
    Brian Brubach
    International Workshop on Algorithms in Bioinformatics (WABI), 2016 (slides)
  18. New Algorithms, Better Bounds, and a Novel Model for Online Stochastic Matching
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    European Symposium on Algorithms (ESA), 2016 (long version) (slides)
  19. Improved bound for online square-into-square packing
    Brian Brubach
    International Workshop on Approximation and Online Algorithms (WAOA), 2014 (long version)

Journal Publications

  1. Online stochastic matching: New algorithms and bounds
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    Algorithmica, 2020 (update of our ESA 2016 paper)
  2. Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    Algorithmica, 2019
  3. Algorithms to Approximate Column-Sparse Packing Programs
    Brian Brubach, Karthik A. Sankararaman, Aravind Srinivasan, and Pan Xu
    ACM Transactions on Algorithms (TALG), 2019