Skip to main content

The UChicago – City Colleges of Chicago Data Science Preceptorship Program places recent Ph.D. graduates in teaching roles at both UChicago and the City Colleges and provides them with faculty mentoring and training in creating effective and inclusive learning environments. The program is part of a broader partnership between UChicago and CCC to strengthen STEM educational and career opportunities and create a more diverse field of professionals entering the sciences. In addition to broadening the pipeline for STEM education locally in Chicago, the partnership hopes that this preceptorship program will serve as an adaptable and scalable model for efforts to improve diversity in data, computing and other fields.

We are currently accepting applications for preceptors to start on August 1, 2023. Learn more and apply.


Evelyn  is a preceptor in data science focusing on data science education as a joint instructor for both the University of Chicago and City Colleges of Chicago. She obtained her PhD in Microbiology from the University of Chicago in 2022 and her BS in Biology from Rider University in 2016. She enjoys reading, writing, and talking with friends and family.

Bio: Amanda Kube is a Ph.D. Candidate in the Division of Computational and Data Sciences at Washington University in St. Louis working with Dr. Sanmay Das in the Department of Computer Science and Dr. Patrick Fowler in the Brown School. She received her B.S. in Psychological and Brain Sciences and Mathematics with a concentration in Statistics from Washington University in St. Louis where she also received an M.S. in Data Analytics and Statistics. Her research interests involve the intersection of computation and the social sciences. Her current work combines machine learning and human decision-making to inform fair and efficient service allocations for homeless families.

Talk Title: Integrating Human Priorities and Data-Driven Improvements in Allocation of Scarce Homeless Services to Households in Need

Talk Abstract: Homelessness is a major public health issue in the United States that has gained visibility during the COVID-19 pandemic. Despite efforts at the federal level, rates of homelessness are not decreasing. Homeless services are a scarce public resource and current allocation systems have not been thoroughly investigated. Algorithmic techniques excel at modeling complex interactions between features and therefore have potential to model effects of homeless services at the individual level. These models can reason counterfactually about the effects of different services on each household and resulting predictions can be used for matching households to services. The ability to model heterogeneity in treatment effects of services provides the potential for “precision public health” where allocation of services is based on data-driven predictions of which service will lead to better outcomes. I discuss the scarce resource allocation problem as it applies to homeless service delivery, and the ability to improve upon the current allocation system using algorithmic techniques. I compare prediction algorithms to each other as well as to the ability of the general public to make these decisions. As homeless services are scarce public goods, it is vital to ensure allocations are not only efficient, but fair and ethical. I discuss efforts to ensure fair decisions and to understand how people prioritize households who should receive scarce homeless services. I also discuss future work and next steps as well as policy implications.