Pyung Kim
I am a Ph.D. candidate in Public Policy and an M.S. candidate in Geospatial Information Science at the University of Texas at Dallas. My research lies at the intersection of geography, public policy, and data science. My work has three primary threads:
- Neighborhood Effects: Investigating how neighborhood environments shape health and economic well-being.
- Spatial Causal Inference: Developing spatial analytical methods to understand why policy impacts differ across locations.
- AI for Public Policy: Applying machine learning to predict where interventions are most needed and to advance anticipatory governance.
As a PI and GIS specialist, I have contributed to numerous grant-funded projects supported by U.S. federal agencies such as the Social Security Administration (SSA) and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). I have also worked on projects funded by Korean institutions, including the Korea Disease Control and Prevention Agency (KDCA), the National Institute of Environmental Research (NIER), the Ministry of Health and Welfare (MoHW), and the National Research Foundation of Korea (NRF).
Biography
Pyung Kim is a graduate researcher at the Spatial Health AI Research Partnership (SHARP) at the University of Texas at Dallas. Before joining UT Dallas, he worked as a research assistant at the Korea Institute of Public Administration, a national research institute under the Prime Minister’s Office (2020). He earned an M.A. in Public Administration from Yonsei University (2020) and graduated summa cum laude with a B.A. in Public Administration from Ajou University (2018). Throughout his academic career, he has received several merit-based fellowships, including the Brain J. L. Berry Scholarship (the highest honor in his doctoral program), the Brain Korea 21 PLUS Scholarship, and the Next Century Humanities Scholarship (National Excellence Scholarship of Korea).
Research Interests
- Neighborhood Effects
- Environment, Health, and Disability
- Spatial Data Science and Machine Learning
- Spatial Causal Inference and Policy Evaluation