Vahideh Manshadi - Research Focus

Market design for social good: The wide adoption of technology-based solutions in the nonprofit and public sectors has enabled these sectors to collect detailed data and implement automated, optimized decision-making processes. This has given rise to the new and growing subfield of “market design for social good” that aims to expand the boundaries of algorithmic decision-making beyond the private sector. In this line of work, we aim to engage with nonprofit and governmental agencies to study their systems through a mathematical and computational lens and help them improve societally important outcomes. As one concrete example, we have recently collaborated with VolunteerMatch (https://www.volunteermatch.org/), an online volunteer labor market, and redesigned their recommendation algorithm. As another example, we study education platforms that help students with the school choice process. 

Algorithmic Fairness: In allocating various resources (e.g., job opportunities) and services (e.g., urgent care), issues of equity and inclusion have become front and center. Further, simplicity and transparency in making these decisions are highly desirable. This combination gives rise to many important yet unexplored allocation problems, especially in dynamic environments, that we aim to study. As one example, we study the problem of sequential search (motivated by algorithmic hiring) under fairness and diversity constraints, and we design a nearly optimal, interpretable, and computationally efficient algorithm. As another example, we study the equitable allocation of a divisible good in a dynamic setting and design an algorithm that is simultaneously optimal and transparent.