BSCD Summer Public Health Research Fellowship - 2026
This internship is part of the University’s Jeff Metcalf Internship Program. Click here to learn more about the program, its benefits, and the UChicago community of supporters. By applying to this internship, you agree to follow the Student Recruiting Guidelines.
Please make sure that if selected for an interview, you communicate to your prospective host organization/employer where you will be physically located during the internship, as your location may affect your (or your host organization/employer’s) ability to pursue this opportunity.
If you are an international student, please visit the OIA website as soon as possible to familiarize yourself with your work authorization eligibility and requirements. If you’d like to make an appointment with your international adviser, please visit this page.
This role is not benefits-eligible.
Fellowship Award Amount: $5,500
- Please complete the BSCD Fellowship Preference Form, which is required if you're applying for this fellowship:
- Please download the Educational Assignment form from the attachments section. Complete the form to the best of your ability. If applicable, you may also have your faculty mentor complete their designated portion. Finally, upload the completed form along with your other application materials.
BSCD Undergraduate Summer Fellowship in Public Health Research
The University of Chicago Department of Public Health Sciences (PHS) seeks to engage college students in mentored research projects in public health. PHS is a multidisciplinary department in the Biological Sciences Division that includes core public health fields including biostatistics, epidemiology, health economics, health services research, health behavior, and global health. The department mission is to improve the health of human populations and reduce disparities by expanding knowledge of factors that affect health, by advancing diverse methods for carrying out such research, and by training the next generation of innovative public health scholars and professionals. PHS faculty design and implement observational and experimental studies in both community and clinical settings, and develop and implement complex analytic methods to understand the determinants of health, the efficacy of interventions, and the structure and financing of health care at the population level.
A primary objective of the BSCD Undergraduate Summer Fellowship is to provide undergraduate students an immersive research experience through close interactions with faculty, research teams, and research projects. Projects will focus on interdisciplinary topics that bring biostatistical, quantitative and qualitative methods to improve understanding of complex problems in population health and develop new solutions.
The Fellowship covers a $5500 stipend, plus the $350 Student Life fee for the summer research period.
Duties and Responsibilities
Fellowships will be 10 weeks in duration and based in Chicago. Fellows will work with their faculty mentors on research projects. Project descriptions are provided below. Applicants need to identify interest in working on one or more of the projects in their application. Projects typically involve data analysis using a computer except where noted.
Requirements
Requirements vary based on the project. Please see the project descriptions.
Class Level Eligibility
Eligibility varies based on the project. Please see the project descriptions.
Required Materials
Applications should include the following:
- Statement of Interest or Cover Letter: Approx. 250 words. Please state here the project (or multiple projects), to which you are applying (see Project Descriptions below for full list).
- Resume or CV
- Unofficial Transcript
- Please complete the BSCD Fellowship Preference Form, which is required if you're applying for this fellowship:
- Please download the Educational Assignment form from the attachments section. Complete the form to the best of your ability. If applicable, you may also have your faculty mentor complete their designated portion. Finally, upload the completed form along with your other application materials.
Please submit all materials as PDF files.
Expiration Date
March 20, 2026
Interviews
Shortlisted candidates will be interviewed by a faculty panel.
Topic 1: Breast Cancer Health Disparity
Breast cancer is the most common malignancy affecting women in the U.S. and the world. There is a gap in breast cancer mortality between African Americans and European Americans. We have an on ongoing program aimed at understanding the socioeconomic, biological, genomic, and health care delivery factors affecting racial disparity, and developing intervention to eliminate disparity and improve health outcomes of all breast cancer patients. We have conducted surveys to inquiry quality of life among breast cancer patients during Covid-19 pandemic and will conduct another yearly survey on quality of life after the peak of another contagious variant. Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
- Epidemiological questionnaire development, interview, and data entry;
- Conducting systematic reviews of the literature; and
- Data clean and analysis.
REQUIREMENTS: Experience manipulating datasets using a statistical package or programming language (e.g. R, Stata, SAS, Python) is preferred.
CLASS LEVEL ELIGIBILITY: UChicago Undergraduate students at all levels are eligible.
FACULTY SPONSOR: Dezheng Huo (https://profiles.uchicago.edu/profiles/display/37017)
Topic 2: Role of Genetics in How Environment Affects Cancer Risk
Risk for cancer and other complex diseases is influenced by inherited genetic risk factors as well as lifestyle and environmental exposures. Ongoing research in the Department of Public Health Sciences is focused on understanding how genetic variation influences or alters the effects of environmental exposures and biomarkers on human health and biology. Areas of ongoing research include (1) telomere length as a biomarker of aging and cancer risk, (2) methods for assessing causal relationships among risk factors, biomarkers, and disease, (3) genome-wide association studies, and (4) susceptibility to the effects of environmental exposure to arsenic, a known carcinogen. Long term goals are to reveal biological mechanisms of disease susceptibility, identify potential targets for pharmacological intervention, and provide strategies for identifying high-risk individuals. Undergraduate students having taken statistical coursework can participate in conducting statistical analyses of genetic and environmental data to understand the determinants of health outcomes in the context of large epidemiological datasets.
REQUIREMENTS: Prior coursework in statistics or epidemiology and some experience using statistical software are required. Prior coursework in genetics is preferred, but not essential.
CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.
FACULTY SPONSOR: Brandon Pierce
(http://health.bsd.uchicago.edu/PersonProfile/Brandon-Pierce)
Topic 3: Cancer Prevention to Eliminate Disparities
Preventing and eliminating cancer disparities is key to achieving optimal and equitable health for all populations. The availability of screening tests to detect breast, cervical, colorectal and lung cancer early, the HPV vaccine to prevent HPV-related cancers, and programs to help patients stop smoking are great public health accomplishments; however, there are segments of the population that still do not receive the full benefits of these behaviors. All of these health behaviors require individuals to interact with health care provider teams and systems. Effective interventions must take into account the local community and policy context and must be easy to implement and sustain. Further, as new technologies (e.g., home-based HPV self-sampling) prove effective and are incorporated into clinical guidelines, the need for appropriate and effective communications to transfer knowledge from “bench to bedside” will be even greater in order to maximize the potential of these new technologies in reducing cancer morbidity and mortality. Dr. Tiro leads the Center to Eliminate Cancer Inequity in the U Chicago Comprehensive Cancer Center. She has several projects and collaborations to understand multilevel determinants of these cancer prevention behaviors and design and evaluate interventions promoting them in Chicagoland populations. Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
- Conducting systematic reviews of the literature on the effectiveness of interventions promoting various cancer prevention behaviors
- Developing surveys and semi-structured interview guides and fielding these data collection tools in local communities to understand factors influencing behavioral adoption
- Analyzing qualitative and quantitative data to understand delivery of HPV self-collection or colorectal cancer screening by federally qualified health centers
REQUIREMENTS: Experience developing data collection tools in Redcap and manipulating datasets using a statistical package (e.g. R, Stata, SAS, NVivo) is required.
CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.
FACULTY SPONSOR: Jasmin Tiro (https://newfaculty.uchicago.edu/staff-directory/jasmin-tiro/)
Topic 4: Road Traffic Injuries in Low- and Middle-Income Countries
Road traffic injuries are the 8th leading cause of death and disability globally. Public Health Sciences has an ongoing program aimed at improving estimates of the public health burden of traffic injuries in low- and middle-income countries, understanding the key risk factors, and developing and evaluating safety interventions. Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
- Acquiring and extracting information from public data sources (household surveys, emergency room surveillance, police records) on the incidence and burden of road traffic injuries.
- Conducting systematic reviews of the literature on the effectiveness of safety interventions
- Developing tools for estimating the prevalence of risk factors (such as speeding behaviors, helmet use, and unsafe infrastructure) from resources like Google Earth and Google Street View.
Requirements: Experience manipulating datasets using a statistical package (e.g., R, Stata, SAS) is required. A solid command of a programming language like Python will be preferred.
Class Level Eligibility: Undergraduate students at all levels are eligible.
Faculty Sponsor: Kavi Bhalla (https://health.uchicago.edu/faculty/kavi-bhalla-phd)
Topic 5: Deceased Donor Organ Allocation
The demand for organ transplants far exceeds the supply of donor organs. The Public Health Sciences department conducts ongoing research into the possible improvements that can be made to the allocation of deceased donor organs, with the aim of improving the equity and efficiency of the organ transplantation system. Undergraduate researchers can contribute to this goal by participating in the following types of activities under faculty supervision:
- Identifying policy mechanisms within deceased donor liver, kidney, and heart allocation that may be inefficient or inequitable by reviewing policy documents and the broader literature.
- Organizing datasets on organ transplant donors, candidates, and recipients.
- Performing statistical and survival analyses to evaluate the outcomes of existing or proposed allocation policies and interpreting and visualizing the results.
Requirements: Experience in data wrangling and analysis using R is required, including an ability to generate reproducible code. A familiarity with statistical methods such as regression and survival analysis, such as cox proportional hazard modeling and Kaplan-Meier functions, is preferred.
Class level Eligibility: Undergraduate students at all levels are eligible.
Faculty sponsor: William Parker (https://pbhs.uchicago.edu/faculty/william-f-parker-md-phd)
Topic 6: Multi-center ICU data science
The Common Longitudinal ICU data Format (CLIF) is a collaborative initiative designed to standardize and enhance critical care research across health systems by implementing a unified ICU data structure. With partners spanning 62 hospitals across 12 U.S. health systems, CLIF supports longitudinal analysis of ICU care while maintaining strict data privacy through federated analytics. Summer fellows will join ongoing CLIF research projects focused on variation in ICU therapies, ventilator practices, sepsis phenotyping, and data-driven modeling of clinical decisions. Past projects include:
- Treatment Variation: Management of empyema with positive cultures, variation in ventilation strategies, use of paralytics in acute hypoxemic respiratory failure, noninvasive respiratory support outcomes, epidemiology of SAT and SBT practices, early mobilization eligibility across ICUs, prone positioning in hypoxemic patients
- Antibiotics & Resistance: Epidemiology of antibiotic days of therapy, ribavirin use in RSV-positive hospitalized adults
- Modeling & Phenotyping: temperature trajectory subphenotypes after CAR T-cell therapy, development of the ICU vulnerability index for ICU patients
- Federated & reinforcement learning: FLAME-ICU federated learning project, personalized respiratory support using deep reinforcement learning, CLIF AI Vent Assistant
- Policy & Outcomes: Epidemiology of code status orders, refining WBC and temperature thresholds in early sepsis, effect of nocturnal sedation on extubation readiness, ICU readmissions across health systems, performance of clinical deterioration risk scores in patients with and without cancer
- Disparities & Access: Language-based disparities in ICU care
Requirements: Students should be proficient in Python and/or R for data wrangling, statistical modeling, and analysis, and be comfortable using visualization tools such as matplotlib, seaborn, or ggplot2, as well as Git for version control. The role involves cleaning and transforming large, complex datasets; developing and maintaining ETL pipelines that convert site specific EHR data into the CLIF format; and generating longitudinal datasets from harmonized CLIF tables. Fellows will apply statistical methods—including regression, survival and competing risk models—and may engage with advanced approaches such as federated learning or reinforcement learning.
Class Level Eligibility: Undergraduate students at all levels are eligible.
Faculty Sponsor: William Parker (https://pbhs.uchicago.edu/faculty/william-f-parker-md-phd)