Please join us for a UW Data Science Seminar event on Tuesday, October 25th from 4:30 to 5:20 p.m. PDT. The seminar will feature two projects from our Data Science for Social Good 2022 summer program: Aziza Mirsaidova and Cheng Ren will discuss “Exploring new understanding of the cost of living at a basic needs level using the Self-Sufficiency Standard database,” and Ihsan Kahveci will discuss “Tracking family and intergenerational poverty using administrative data.”
“Exploring new understanding of the cost of living at a basic needs level using the Self-Sufficiency Standard database”
Abstract: The best-known measure of income adequacy, the Official Poverty Measure, (OPM) is too low with flawed and problematic methodology. Since its original development in the 1960s, societal changes have not been reflected in the measure’s calculation. The OPM does not vary geographically or by age of children, meaning someone is considered “poor” at the same threshold in Sioux City, Iowa and South Manhattan, New York. As the OPM is used to set eligibility for critical benefits (e.g. food assistance, child care subsidies, or housing vouchers), many families unable to afford their basic needs are not considered “in need” by the OPM and cannot access these supports.
Biographies: Aziza Mirsaidova is a Master’s student at Northwestern University, McCormick School of Engineering. She is pursuing her studies in Artificial Intelligence with a focus on Natural Language Processing and Understanding. She is interested in learning and researching computational approaches that enable computers to understand human language and break down communication barriers between people and language technologies. At Northwestern, she is involved in multiple research projects including Tiilt lab where she is involved in enhancing the source separation process for the Blinc app and leading user research study groups to understand and improve the student’s collaboration process using AI integrated technologies.
Cheng Ren (he/his/him) is a Ph.D. student in Social Welfare and Data Science at the University of California, Berkeley. He is also a Senior Data Science Fellow at D-Lab at UC Berkeley. Prior to Berkeley, he earned his master’s degree at Case Western Reserve University in Social Work and then worked as a data scientist in several social welfare related projects.
“Tracking family and intergenerational poverty using administrative data”
Abstract: This project will examine whether the Seattle $15 minimum wage policy reduced poverty or affected intergenerational economic mobility. Research to date on the impacts of the Seattle law have largely focused on individual workers’ experiences. This project will create the data needed to examine impacts of the policy on poverty.
Why do we need new data? Poverty is a household-level measure and cannot be known from the individual records used in pervious work. A single worker earning $25,000 per year is above the poverty line; that same income would render a sole earning parent with a spouse and two children “in poverty.” On the other hand, this worker could be a secondary earner in a wealthy household. Hence to measure poverty, we need to know how individual earners are grouped into households or families.
Biography: Ihsan Kahveci is a third-year doctoral student in Sociology at the University of Washington and an affiliated student at the Max Planck Institute for Demographic Research. He holds an MA in Sociology from the University of Washington and a BA in Management from Bogazici University, Turkey. Ihsan’s research focuses on the intersection of public health and social networks. His mixed methods research integrates computational sociology, demography and social epidemiology. His dissertation focuses on vaccine-hesitancy and health misinformation, especially in non-Western contexts. His work also considers the religious and political dynamics behind adherence to COVID-19 public health measures.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
The 2022-2023 seminars will be virtual, and are free and open to the public.