Research

I work on algorithms for problems from the intersection of computer science and economics, including fair resource allocation, algorithmic game theory, and computational social choice. I am interested in the application of these ideas in algorithmic fairness within artificial intelligence and machine learning tasks.

I am motivated by the intersection of algorithmic and normative challenges: what counts as a good and fair solution to problems in resource allocation, voting, or machine learning, and how can we compute such solutions? Many of the relevant techniques for my work come from game theory and approximation algorithms. While I enjoy formalizing problems mathematically, I am also particularly interested in how algorithmic techniques from computer science can yield practical insights for applied systems in the real world, a topic of ever more importance as algorithms take on a more pervasive role in the life of our society.

Publications by Date

  1. Tian, M., Nianli, P., Fan, Z., Fain, B. Welfare and Fairness in Multi-objective Reinforcement Learning. Accepted for publication at Autonomous Agents and Multi-Agent Systems (AAMAS) (2023). [arxiv link].
  2. Shen, Z., Wang, Z., Zhu, X., Fain, B., Munagala, K. Fairness in the Assignment Problem with Uncertainty. Accepted for publication at Autonomous Agents and Multi-Agent Systems (AAMAS) (2023) [arxiv link].
  3. Pujol, D., Sun, A., Fain, B., Machanavajjhala, A. Multi-Analyst Differential Privacy for Online Query Answering. Accepted for publication in Proceedings of the 49th International Conference on Very Large Data Bases (VLDB) (2023). pp. 816-828. [acm dl link].
  4. Lin, J., Chen, C., Chmielewski, M., Zaman, S., Fain, B. Auditing for Gerrymandering by Identifying Disenfranchised Individuals. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22) (2022). pp. 1125–1135. [acm dl link]
  5. Pujol, D., Wu, Y., Fain, B., Machanavajjhala, A. Budget Sharing for Multi-Analyst Differential Privacy. In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB) (2021), pp. 1805-1817 [arxiv link].
  6. Lyu, L., Fain, B., Munagala, K., Wang, K. Centrality with Diversity. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM) (2021), pp. 644-652. [acm dl link]
  7. Fain, B., Fan, W., Munagala, K. Concentration of Distortion: The Value of Extra Voters in Randomized Social Choice. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI) (2020), pp. 110-116. [arxiv link]
  8. Chen, X., Fain, B., Lyu, L., Munagala, K. Proportionally Fair Clustering. In Proceedings of the 36th International Conference on Machine Learning (ICML) (2019), pp. 5029–5037. [arxiv link]
  9. Fain, B., Goel, A., Munagala, K., and Prabhu, N. Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (2019), pp. 1893-1900. [arxiv link]
  10. Fain, B., Munagala, K., and Shah, N. Fair Allocation of Indivisible Public Goods. In Proceedings of the 2018 ACM Conference on Economics and Computation (EC) (2018), pp. 575-592. [arxiv link]
  11. Fain. B., Goel, A., Munagala, K., and Sakshuwong, S. Sequential Deliberation for Social Choice. In Proceedings of the 13th International Conference on Web and Internet Economics (WINE) (2017), pp. 177-190. [arxiv link]
  12. Mayuresh K., Fain, B., Munagala, K., and Babu, S. ROBUS: Fair Cache Allocation for Data-parallel Workloads. In Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD) (2017), pp. 219-234. [arxiv link]
  13. Fain, B., Goel, A., and Munagala, K. The Core of The Participatory Budgeting Problem. In Proceedings of the 12th International Conference on Web and Internet Economics (WINE) (2016), pp. 384-399. [arxiv link]