Dr. Sangmi Kim is an Assistant Professor on the tenure track at the Nell Hodgson Woodruff School of Nursing. She obtained her doctoral degree from the University of Pennsylvania School of Nursing and completed her postdoctoral training at Duke University School of Nursing.

Dr. Kim is deeply dedicated to promoting the health and well-being of minoritized and oppressed women. Her research focuses on chronic stress and trauma among pregnant women and women impacted by intimate partner violence (IPV), with a specific emphasis on health equity. She employs data science, including big data analysis, machine learning, and digital health technologies, as well as conducts interdisciplinary research to inform, enlighten, and generate innovative solutions.

Her goal is to unveil the chronic stress mechanisms behind racial/ethnic disparities in preterm birth. Dr. Kim develops race/ethnicity-specific, interpretable machine learning models to more accurately predict preterm birth by considering the complex and interactive relationships between chronic stressors. Additionally, she aims to prevent IPV and support individuals experiencing IPV by creating a trauma-informed support infrastructure in digital spaces.

Dr. Kim co-leads the Violence Prevention Task Force at the CDC-funded Injury Prevention Research Center at Emory (IPRCE). She is a member of the Center for Data Science at the School of Nursing and a Board Member of the Center for Artificial Intelligence Learning (CAIL) at Emory University.

1. Kim, S., Brennan, P.A., Slavich, G.M. et al. (2024). Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model. BMC Pregnancy Childbirth, 24, 438. https://doi.org/10.1186/s12884-024-06613-w

2. Afzal, H. B., Jahangir, T., Mei, Y., Madden, A., Sarker, A., & Kim, S. (2024). Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models. Frontiers in public health, 11, 1309490. https://doi.org/10.3389/fpubh.2023.1309490 

3. Guo, Y., Kim, S., Warren, E., Yang, Y. C., Lakamana, S., & Sarker, A. (2023). Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023, 254–260. 

4. Kim, S., Warren, E., Jahangir, T., Al-Garadi, M., Guo, Y., Yang, Y. C., Lakamana, S., & Sarker, A. (2023). Characteristics of Intimate Partner Violence and Survivor’s Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence. Journal of Interpersonal Violence, 38(17–18), 9693–9716. 

5. Al-Garadi, M. A., Kim, S., Guo, Y., Warren, E., Yang, Y. C., Lakamana, S., & Sarker, A. (2022). Natural language model for automatic identification of Intimate Partner Violence reports from Twitter. Array (New York, N.Y.), 15, 100217. https://doi.org/10.1016/j.array.2022.100217

6. Kim, S. (2022). Different maternal age patterns of preterm birth: Interplay of race/ethnicity, chronic stress, and marital status. Research in Nursing & Health, 45(2), 151-162. https://doi.org/10.1002/nur.22205 

7. Kim, S., Sarker, A., & Sales, J. M. (2021). The use of social media to prevent and reduce intimate partner violence during COVID-19 and beyond. Partner Abuse, 12(4), 512-518. DOI: 10.1891/PA-2021-0019 

8. Kim, S., Im, E. O., Liu, J., & Ulrich, C. (2020). Maternal age patterns of preterm birth: Exploring the moderating roles of chronic stress and race/ethnicity. Annals of Behavioral Medicine, 54(9), 653-64. doi: 10.1093/abm/kaaa008 

9. Kim, S., Im, E. O., Liu, J., & Ulrich, C. (2019). Factor structure for chronic stress before and during pregnancy by racial/ethnic group. Western Journal of Nursing Research, 41(5), 704-727. doi: 10.1177/0193945918788852

I have taught the following courses for the prelicensure program:

NRSG-539MN Optimal Wellness Across Lifespan

NRSG 322 Health Promotion and Well-Being

NRSG 371 Evidence-Based Nursing Practice

Race/Ethnicity-Specific Algorithms of Chronic Stress Exposures for Preterm Birth Risk: Machine Learning Approach 

Role: PI
Grant Number: K01NR019651 
Funding Agency: NIH/NINR 
Total Costs: $440,863 
Funding Period: 05/11/2022-04/30/2025 

The Intersecting of Climate Change and Gender-Based Violence: An Examination of the Social and Environmental Determinants of Displacement from Natural Disasters Among Women Experiencing Violence” (PI: Ruiz) 

Role: Co-Mentor 
Funding Agency: Pediatric and Reproductive Environmental Health Scholars - Southeastern Environmental Exposures and Disparities (PREHS-SEED) 
Funding Period: 2024-2026

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2024, Top Cited Article 2022-2023 in Research in Nursing & Health (Title: “Different Maternal Age Patterns of Preterm Birth: Interplay of Race/Ethnicity, Chronic Stress, and Marital Status”) 

2023, Top 2 Publication in 2022 in Research in Nursing & Health (Title: “Different Maternal Age Patterns of Preterm Birth: Interplay of Race/Ethnicity, Chronic Stress, and Marital Status”)

2023, “A Patient-Focused Collaborative Hospital Repository Uniting Standards for Equitable AI” (CHoRUS) AI in Clinical Care Bootcamp Travel Grant ($1,000), National Institutes of Health 

2021, Excellent Service Award for Conference Planning, Emory University NHWSN/AAPINA 2021 Joint International Research Conference 

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