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Working Groups

EAAMO-Bridges working groups bring together academic researchers from various disciplines to tackle problems in real-world application domains through online working groups. These groups work towards research, implementation, and advocacy projects throughout the year. Notes from the original reading group can be found here.

If you are interested in being involved or pitching problems to these working groups in any of the domains below, please contact the Working Group Organizers at bridges@eaamo.org.

If you want to check our previous working groups, please check out this page.

Algorithms in Household Finance

Advances in machine learning and automated decision-making are transforming how financial services are designed, priced, and delivered. These changes raise important questions about efficiency, fairness, consumer welfare, and regulation. Our aim is to bring together researchers and practitioners interested in understanding these developments and their implications.

Environment

We study how tools from algorithmic decision, machine learning, and causal inference can help us measure and identify the effects of environmental degradation and propose new pathways to more sustainable societal development. We will discuss how market design can be helpful when thinking about carbon bonds or biodiversity conservation, as well as how we can use a combination of ML and causal inference to identify adaptation policies to a changing environment.

Decolonization of STEM Curriculum

We seek to develop a set of curricula for undergraduate students in courses around Algorithms, Mechanisms, and Optimization that intersect various fields (i.e., healthcare, art, and law) with an idea to uplift and acknowledge the efforts of marginalized folks in these spaces while empowering students to engage with these fields in a way that deconstructs and resists colonial structures and ideologies.

Urban Data Science & Equitable Cities

We focus on the computational analysis of urban data to explore and address inequities in urban life, hosting speakers, studying papers, and workshopping late-stage research.

Inequality

We study how computational methods can either address or exacerbate inequality, both globally and for vulnerable populations in the United States.