First awarded 121 years ago, the Nobel Prizes are arguably the most famous and prestigious prizes in the world for contributions to humankind. Unfortunately, the group of Nobel laureates does not itself reflect humankind's diversity. The exclusion of minoritized/marginalized groups among prizewinners is already well-known. What lacks is an understanding of the specific loci of decision-making responsible for this exclusion. In this research-to-action project, we will gather publicly available data not just about Nobel Prize winners, but about all known nominees, nominators, and members of the prize selection committees. Focusing on gender and race/ethnicity, we will construct networks to represent the nomination-selection pipeline, looking for patterns of demographic association and other insights that might shed light on mechanisms of exclusion. Depending on the results and conclusions of the study, we will work to leverage our results into change. You do not need to have any particular mathematical or computational background to participate in this project, though experience programming in a language such as R or Python will be very helpful. This project is best suited for participants who are enthusiastic about learning and/or applying data scraping and network analysis techniques, and who are accepting of the idea of translating research results into activism, when warranted.

Academic mathematics has a long and well-documented history of excluding female, queer, BIPOC, disabled, and first-generation scholars from career advancement. Math is not a monolith, however. Different institutions and mathematical subfields have experienced different degrees of success in diversifying their faculty and PhD cohorts. In this project, we will study the evolution of female gender representation in mathematical institutions and subfields. We will use a data set compiled from the Mathematics Genealogy Project, combined with inferred gender from several automated services. This data was collected by Ben Brill (UCLA), with guidance from Phil Chodrow (UCLA), Mason Porter (UCLA), and Heather Zinn Brooks (Harvey Mudd).

Using this data set, we will build understanding of how some subfields and institutions have reached relatively high levels of female gender representation, while others remain badly male-dominated. We will study the possible roles of accumulation mechanisms, homophily, and prestige as mechanisms supporting or inhibiting female gender representation in mathematics. A long-term aim of this work is to develop actionable strategies to promote inclusive representation by leveraging these mechanisms.

Useful areas of expertise for this project include network analysis and modeling, inferential statistics, dynamical systems, and data visualization.

Rising housing costs are a subject of much discussion in many major cities. While policy proposals around housing generally agree that an increase in housing supply is needed, there is a great deal of disagreement on how best to achieve this increase. In this project, we aim to build on an existing quantitative literature around measuring gentrification and displacement by studying simple theoretical models of potential solutions. Specifically, we are interested in modeling the effects of policies around public, low-income, and affordable housing, as well as zoning restrictions.

This project will likely combine spatial analysis, housing policy, census data, and dynamical systems. If you have interest or expertise in these areas, please feel welcome to join!

References to and use of the term 'social justice' have increased across various disciplines of study seeking to advance equity. If we take a view of equity in relation to justice that centers the ability to sustain and improve a social impact in increasingly inequitable and marginalizing contexts, many longer-term effects of movements for justice must be centered to their material outputs. There are related terms - such as distributive justice and transitional justice - that present cross-disciplinary opportunities for mathematics and data science communities to examine more concrete (read: materializing/materialized) conceptions of change and justice.

Distributive justice relates to fairness in the allocation and distribution of goods or services with a particular focus on outcomes, which may benefit inquiries in more localized contexts. Transitional justice, more broadly, considers the shift from discourses on ethics and social values to broad policies and material outputs that redress legacies of human rights abuses, especially through law and public policy. For this project, we will examine disciplinary approaches to distributive justice and transitional justice in order to consider the role of mathematics, data science, and network analysis in advancing interdisciplinary methodologies.

No prior knowledge will be needed.