<aside> 📖 The data-driven analytics lab is directed by Dr. Sunmee Kim, Assistant Professor of Quantitative Psychology at the University of Manitoba. Our goal is to develop and implement cutting-edge data analytics tools for data of diverse formats, structures, and scales, emerging from the social, behavioural, and health sciences.

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Academic CV

👋 Hi, I’m Sunmee. Data analytics in Psychology and related areas can be achieved through a variety of methods and techniques, such as statistical analysis, machine learning algorithms, and data visualization.

The key idea behind data-driven analytics is that data can offer a more meaningful and accurate basis for decision making compared to relying solely on human judgment. This opens up opportunities for novel and creative collaborative work with the researchers in substantive areas.

• Research Focus & Funding: I’m using and studying a wide array of statistical methods and computational algorithms in the domains of multivariate statistics, predictive modeling, data reduction, non-parametric methods, regularization, and longitudinal data methods. This work is supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), along with additional support from University of Manitoba funding programs.
• Programming & Tools: In my research, teaching, and student supervision, I primarily use R, reflecting my expertise and familiarity with its extensive capabilities. I am also proficient in other statistical software such as SAS, SPSS, and MATLAB, and use them as needed. Explore my GitHub to see examples of my coding projects, including the R package {gesca} that I developed and maintain, as well as other implementations of innovative statistical methods.

Generalized Structured Component Analysis Structural Equation Modeling

• Aging Research & Longitudinal Methods: Since 2023, I have been appointed as a Research Affiliate at the Centre on Aging in the University of Manitoba. My research aligns closely with the Centre’s focus, particularly in big data and predictive analytics for longitudinal data, as well as data-driven approaches to identifying risk and protective factors related to cognitive aging and late-life depression—especially using data from the Canadian Longitudinal Study on Aging (CLSA). See the attached poster for an example from my Centre on Aging Speaker Series presentation.

  Centre on aging speaker series.pdf

More About Kim and Lab

(Old) Kim, Faculty Web →

🔗 LinkedIn Profile →

📨 Contact →

🧐 Psychology at U of M →

📝 Academic CV →