Siddharth’s recent work considers fairness from a computation lens.
For example, one of his recent works shows that one can compute outcomes, in economic systems, which are efficient and fair at the same time, i.e., the seeming incompatible properties of efficiency and fairness can be achieved together.
Given that fairness is a fundamental consideration in many resource-allocation problems, these results can potentially influence allocation policies in practical settings. Siddharth is also interested in developing fairness guarantees in machine-learning contexts such as clustering and classification.
Faculty Member: Siddharth Barman [CSA]