Research

In recent years, my research has expanded rapidly, focusing on the following themes.

Modeling and inference for high-dimensional time series (HDTS): Topics of interest include a broad range of established time series methods, such as variable selection for sparse modeling, dimension reduction, factor models, clustering, graphical models, spectral analysis, change point detection, nonlinear models, and continuous-time models. These methods are extended to high-dimensional settings, with particular emphasis on incorporating network structures. Ongoing research focuses on dimension reduction for HDTS modeling, hypothesis testing for nonlinear HDTS models, graphical modeling of discrete-valued HDTS, and clustering of networks derived from HDTS.

Computational and theoretical aspects of statistical machine learning: Topics of interest are motivated by the challenges of fitting high-dimensional models, which require efficient and scalable algorithms. From a high-dimensional statistical perspective, this research also investigates the theoretical foundations of these methods, including the statistical properties of the models and the computational efficiency of the associated algorithms. Ongoing research focuses on coordinate descent algorithms for regularized regressions, empirical risk minimization for dimension reduction, and clustering of high-dimensional functional data.

Methodological applications in scientific domains: The topics of interest are motivated by problems arising in specific scientific domains and the development of statistical methodologies tailored to those problems, even when the underlying models and algorithms are relatively simple. This perspective is rooted in my belief that statistics cannot exist in isolation. My research develops and applies statistical methods to address problems in psychology, neuroscience, psychiatry, finance, and economics, with an emphasis on providing practically useful solutions. Ongoing research focuses on graphical modeling for analyzing individual differences in social and personality psychology and characterizing cognitive flexibility by integrating behavioral and neuroimaging data through statistical modeling.

I am always happy to discuss research ideas and opportunities for collaboration with students, researchers, and anyone interested in related themes.

Publications

• Kim, Y., and Baek, C. (2026). Latent Community Paths in VAR-Type Models via Dynamic Directed Spectral Co-Clustering. To appear in Journal of Time Series Analysis.

• Kim, Y., Basu, S., and Banerjee, S. (2026). Identification of Temporal Hotspots of Distributional Change in Stress and Passive mHealth Data. Sensors, 26(21), 6739.

• Kim, Y.*, Deb, N.*, and Basu, S. (2026). cxreg: An R Package for Complex-Valued Lasso and Graphical Lasso. Journal of Open Statistical Software, 11(124), 9698.

• Kim, Y., Dücker, M. C., Fisher, Z. F., and Pipiras, V. (2026). Latent Gaussian Dynamic Factor Modeling and Forecasting for Multivariate Count Time Series. Journal of Time Series Analysis, 47(1), 43–58.

• Qiu, Y., Carter, E., Benda, N., Sirey, J. A., Kim, S., Kim, Y., Yu, Z., Kiosses, D. N., Marino, P., Alexopoulos, G. S., Faith, G.-D., and Banerjee, S. (2025). Improving Adherence to Psychotherapy and Clinical Outcome in Patients with Late-Life Depression through Gamified mHealth Technology. American Journal of Geriatric Psychiatry, 34(4), 568–579.

• Kim, Y., Basu, S., and Banerjee, S. (2025). A Co-Segmentation Algorithm to Predict Emotional Stress from Passively Sensed mHealth Data. Statistics in Medicine, 44(10–12), e70099.

• Kim, Y., Fisher, Z. F., and Pipiras, V. (2024). Group Integrative Dynamic Factor Models with Application to Multiple Subject Brain Connectivity. Biometrical Journal, 66(8), e202300370.

• Fisher, Z. F., Kim, Y., Pipiras, V., Crawford, C., Petrie, D., Hunter, M., and Geier, C. (2024). Structured Penalization of Heterogeneous Time Series. Multivariate Behavioral Research, 59(6), 1270–1289.

• Fisher, Z. F., Kim, Y., Fredrickson, B. L., and Pipiras, V. (2022). Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data. Psychometrika, 1–29.

• Ahn, D., Kim, K.-K., and Kim, Y. (2020). Small-Time Smile for the Multifactor Volatility Heston Model. Journal of Applied Probability, 57(4), 1070–1087.

Preprints

• Crawford, C., Dücker, M. C., Kim, Y., Pipiras, V., and Fisher, Z. F. (2026). Analytic Standard Errors for Latent Gaussian Discrete-Valued Multivariate Time Series. [arXiv] 

• Deb, N.*, Kim, Y.*, and Basu, S. (2026). Inference for High-Dimensional Sparse Spectral Precision Matrices. [arXiv] 

• Bowling, C. B., Levitan, E. B., Ringel, J. B., Kim, Y., Enogela, E. M., Goyal, P., Glover, L., Reid, R.-J., Sterling, M. R., Safford, M. M., and Banerjee, S. (2026). Characterizing Reserve Across Multiple Domains among Older US Adults: Findings from the REGARDS Study. [link]

• Kim, Y., Fisher, Z. F., and Pipiras, V. (2025). Joint Modeling and Inference of Multiple-Subject High-Dimensional Sparse Vector Autoregressive Models. [arXiv] 

• Kim, Y., Loh, P.-L., and Basu, S. (2025). Exact Coordinate Descent for High-Dimensional Regularized Huber Regression. [arXiv] 

A current list of my publications is available on my Google Scholar profile.