Brandon Fain


Interests

I study theoretical computer science at Duke University. I work on algorithms and most of the problems that interest me come from the intersection of computer science and economics, including fair resource allocation, algorithmic game theory, and computational social choice. An area of growing interest to me is the application of these principles in algorithmic fairness to machine learning tasks like classification and clustering.

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, convex optimization, learning, and traditional 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

Fair Allocation of Indivisible Public Goods (arxiv link)
     Brandon Fain, Kamesh Munagala, Nisarg Shah
     EC (Economics and Computation) 2018

Sequential Deliberation for Social Choice (arxiv link)
     Brandon Fain, Ashish Goel, Kamesh Munagala
     WINE (Web and Internet Economics) 2017

ROBUS: Fair Cache Allocation for Data-parallel Workloads (arxiv link)
     Mayuresh Kunjir, Brandon Fain, Kamesh Munagala, Shivnath Babu
     SIGMOD (Special Interest Group on Management of Data) 2017

The Core of The Participatory Budgeting Problem (arxiv link)
     Brandon Fain, Ashish Goel, Kamesh Munagala
     WINE (Web and Internet Economics)

Conference Presentations

Fair Allocation of Indivisible Public Goods
     EC'18 (Economics and Computation) 2018

Sequential Deliberation for Social Choice
     WINE (Web and Internet Economics) 2017

Deliberation for Social Choice
     HCOMP (Human Computation and Crowdsourcing) 2017 Workshop on Mathematical Foundations of Human Computation

The Core of The Participatory Budgeting Problem
     WINE (Web and Internet Economics) 2016