Sunmee Kim

• PSYC 3200 - Thinking Critically About Psychological Research
• PSYC 7210 - Quantitative Methods in Psychology 2
• PSYC 7420 - Multivariate Methods in Psychology

Dr. Sunmee Kim is an Assistant Professor in the Department of Psychology at the University of Manitoba and serves as a research affiliate at the Centre on Aging at the same university.

Dr. Kim is also the Associate Chair of AKCSE-WiSE (The Association of Korean-Canadian Scientists and Engineers-Women in Science and Engineering). She obtained her academic degrees from Korea University, South Korea, including a BS in Statistics, a BA in Psychology (Double Major), and an MS in Mathematical Statistics. Following her passion for quantitative research, she pursued a PhD in Quantitative Psychology & Modelling from McGill University, completing her doctoral studies in 2020.

Dr. Kim's research interests encompass the development of innovative statistical techniques to address the assumptions and limitations of current quantitative methods in the fields of Psychology and Behavioral Data Science. By combining her expertise in psychology and statistical modeling, she aims to contribute to advancing the understanding and application of statistical methodologies in these domains.

• PhD (Quantitative Psychology and Modeling), McGill University, 2020
• MS (Mathematical Statistics), Korea University, 2012
• BEc (Statistics), Korea University, 2010 
• BA (Psychology), Korea University, 2010

Research interests

• Longitudinal data methods
• Knowledge-based dimension reduction
• Multivariate prediction
• Application of advanced quantitative methods

Research summary

I am deeply dedicated to bridging the gap between advanced statistical methods and their practical integration into domain-specific research. One way to achieve this is by developing new statistical techniques. I have proposed solutions to specific challenges within the framework of Extended Redundancy Analysis (ERA), which conducts knowledge-based dimension reduction in prediction models. Within this context, I introduced two extensions of ERA: GEE-ERA and ERA-Tree. Additionally, I have developed novel predictive model indices for ERA and continue to work on extending ERA to address questions within the longitudinal data framework. Another way is through extensive collaborative work with domain experts in Health, Sociology, and Space Systems. Using large epidemiological studies such as the CLSA and HRS, I have collaborated on topics like examining intersectionality in health using data-driven machine learning methods and studying various factors impacting immigrant health and retention. Furthermore, I communicate advanced quantitative concepts in an accessible manner to domain researchers and conduct scoping review research regarding the current status and best practices in methodological approaches within these domains.

Research affiliations/groups

• Centre on Aging, University of Manitoba

• Kim, S., & Hwang, H. (2022). Evaluation of Prediction-Oriented Model Selection Metrics for Extended Redundancy Analysis. Frontiers in Psychology, 13, 821897. doi.org/10.3389/fpsyg.2022.821897
• Kim, S., & Hwang, H. (2021). Model-based recursive partitioning of extended redundancy analysis. British Journal of Mathematical and Statistical Psychology, 74, 567-590. doi.org/10.1111/bmsp.12240
• Kim, S., Lee, S., Cardwell, R., Kim, Y., Park, T., & Hwang, H. (2020). An application of regularized extended redundancy analysis via generalized estimating equations to the study of co-occurring substance use among US Adults. In: Wiberg, M., Molenaar, D., González, J., Böckenholt, U., Kim, J.S. (Eds.), Quantitative Psychology. IMPS 2019. Springer Proceedings in Mathematics & Statistics, vol 322 (pp. 365-376). Springer, Cham. doi-org.uml.idm.oclc.org/10.1007/978-3-030-43469-4_27
• Kim, S., Choi, J. Y., & Hwang, H. (2017). Two-way regularized fuzzy clustering of multiple correspondence analysis. Multivariate Behavioral Research, 52, 31-46. doi.org/10.1080/00273171.2016.1246996
• Kim, S., Cardwell, R., & Hwang, H. (2017). Using R package gesca for generalized structured component analysis. Behaviormetrika, 44, 3-23. doi.org/10.1007/s41237-016-0002-8

• 2024 - Early Achievement Award, The Association of Korean-Canadian Scientists and Engineers (AKCSE)
• 2023 - Women in Science & Engineering (WiSE) Award, The Association of Korean-Canadian Scientists and Engineers (AKCSE)
• 2023 - Invitation to the 1st World Congress of Korean Scientists & Engineers, Ministry of Science and ICT, the government of South Korea
• 2022-2023 - Nominee, Department of Psychology Teaching Award
• 2022 - Invited International Scholar for the Korea University BK21 FOUR Conference on Statistical Learning & Data Science, Korea University, South Korea

I served as a mentor for the 2022-23 International Young Woman Scientist Camp organized by the Association of Korean Woman Scientists and Engineers. In this role, I guided female mentees from developing countries in natural sciences and engineering, providing them with insights on overcoming challenges, addressing personal concerns, and advancing their career development as early-career female scientists from underrepresented regions. In my capacity as the Associate Chair of the AKCSE's WiSE (Women in Science & Engineering) committee, I represented Canada's EDI practices at two international events hosted by the Korean government, including the Global Network of Korean Women in Science and Technology Associations Forum and the inaugural World Congress of Korean Scientists & Engineers. Furthermore, I organized and chaired the WiSE session at the AKCSE's annual conferences in 2023 and 2024, which facilitated discussions on emerging topics and networking between women scientists and leaders from academic, institutional, and government sectors in Canada and Korea.