Schedule, Notes/Handouts & Videos

This session will engage participants in unpacking mathematics teaching and learning focused on racial equity and social justice. Specifically, the session will explore the intersection of mathematics teaching and learning with racial equity and social justice across four critical reasons: 1) building an informed society; 2) connecting mathematics to cultural and community histories as valuable resources; 3) confronting and solve real-world mathematics as a tool to confront inequitable and unjust contexts, and 4) use mathematics as a tool for democracy and creating a more just society.

Recent scholarship warns that computing work has treated problematic features of the status quo as fixed, failing to address and often even exacerbating deep patterns of injustice and inequality. This begs the question: what roles, if any, can computing play to support and advance fundamental social change? Through an analysis informed by critical scholarship, we articulate four such roles -- computing as a diagnostic, formalizer, rebuttal, and synecdoche. We then illustrate how mathematical and computational tools can aid in understanding and tackling poverty and social inequities through the role of computing as a formalizer. We close with a discussion on the Mechanism Design for Social Good (MD4SG) research community, which works to bridge research and practice to ensure that such insights can be leveraged to advance social change.

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The SARS-CoV-2 pandemic has brought about a new level of fear and many more constraints and challenges. This pandemic has clearly amplified the reality of unequal health outcomes for people of color. Racial disparities are pervasive in our health care system. The intersection of numerous diseases is leading to greater morbidity and mortality in existing at-risk groups.

Researchers within many areas of Public Health and Medicine are finding that the roots of discrimination run deep and the dis-ease beneath the diseases are killing from all angles. Mounting epidemiological studies are demonstrating disparities amongst race within both communicable and non-communicable diseases.

From data on maternal-child health to Alzheimer’s and now Coronavirus, the statistics are revealing the unbalance of care and treatment. The data is clear no matter the phase of life the pandemic of racism effects a person from birth to late stage of life.

Within this talk we will aim to discuss many of the underlining roots of disparities throughout Public Health research areas. We will also provide a glimpse at research fighting for equality of health. Lastly, through personal and research experiences in Alzheimer's and COVID-19 we will aim to look at how we can use the mathematical sciences for social and systemic change.

In this talk, I intend to challenge the way in which we are traditionally taught to operationalize race in biomedical research. By introducing a causal perspective, my hope is that we can move away from a descriptive approach and towards an inferential approach that moves us closer to finding effective interventions to reduce racial disparities in health.

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In this talk, I will introduce the mathematics and applications of election procedures and apportionment methods.

Elections are easy when there are only two candidates: vote by majority rule.  For three or more candidates, Kenneth Arrow’s Impossibility Theorem shows that no three-candidate election procedure satisfies a set of reasonable axioms, implying that there is no “best” election procedure.  After discussing the axiomatic approach in voting theory, we will review commonly used election procedures, including the use of ranked choice voting in East Pointe, Michigan as part of the resolution of a Voting Rights Act lawsuit.

In the context of the US House of Representatives, the apportionment problem is to determine the number of representatives each state receives in the House.  We will review the history and mathematics of apportioning the House, including its relationship to the Electoral College.  We will conclude with the recent use of apportionment methods to allocate delegates among candidates in the Democratic and Republican presidential primaries.

Once a decade issues of fair representation in government come to the forefront and that time is now. The census numbers were just released in April and the process of allocation and redrawing district lines has begun. In the first part of this talk, we will give a brief overview of gerrymandering and the voting rights act and introduce the new Geometry and Election Outcome (GEO) metric. This metric is unique in that it uses both election data and map data to quantify gerrymandering.

For the second part of the talk we will discuss alternate voting processes which may better approach the ideal of “one person one vote”.

In this talk, I will introduce the mathematics and applications of bankruptcy problems and matching problems.

When a firm goes bankrupt, the firm’s assets are divided among the firm’s creditors based on how much each creditor is owed.  This is known as the bankruptcy problem.  The history of bankruptcy problems dates back 2000 years to a passage in the Babylonian Talmud.  We will consider the history of the Talmud problem and its relationship to cooperative game theory.  As an application, we will connect bankruptcy problems to the problem of reparations.  Further, we will design a mechanism to apply noncooperative game theory to award travel funds, a problem that is similar to a bankruptcy problem.

David Gale and Lloyd Shapley introduced the stable marriage problem and an algorithm to match spouses in a stable way.  The algorithm had been in use by the National Resident Matching Program (The Match) to match doctors to hospital residency training programs before the Gale-Shapley article. We will review the Gale-Shapley algorithm and discuss the application of algorithms to solve the school choice problem of matching students to public schools (as used in Boston and New York).

Connect and Catalyze