Kun Chen

Kun Chen is a Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. His research bridges advanced statistical methodology with critical applications in healthcare, environmental science, and mental health.

His research focuses on large-scale statistical learning, machine learning optimization, and healthcare analytics, particularly in suicide risk prediction using electronic health records and health information exchanges. Recent work integrates natural language processing with social determinants of health for veteran suicide prediction and develops novel tensor regression methods for longitudinal data with missing observations.

Analysis of his 15 most recent publications reveals a dominant trend in mental health data science (73% of articles), with significant contributions to statistical methodology (53%) including reduced-rank regression extensions and sparse factor modeling. His environmental statistics work (20%) focuses on nanomaterial applications in contaminated agriculture and microbiome-environment interactions.

Scientific Recognition:

• Co-authored seminal 2023 Springer monograph Multivariate reduced-rank regression: theory, methods and applications (2nd Edition)
• Developed rrpack R package for reduced-rank regression (2019)

His collaborative work spans UConn Health, Veterans Affairs, and multiple national consortia, with recent grants supporting data fusion techniques for suicide prevention and Parkinson's disease progression modeling. Current projects include transfer learning frameworks for hospital suicide risk prediction and gut microbiome analysis in neurological disorders.

Dr. Chen maintains active leadership in statistical ecology applications and serves on editorial boards for biostatistics journals, with recent work on quantum dot analysis demonstrating methodological versatility across physical and health sciences.