Seyoung Kim

Seyoung Kim is an Associate Professor in the Department of Epidemiology at the University of Pittsburgh School of Public Health. She holds a PhD in Computer Science from University of California, Irvine (2007), preceded by a BS in Computer Engineering from Seoul National University (2001), and completed postdoctoral training at Carnegie Mellon University (2010). Her methodological research focuses on statistical machine learning for systems genomics, with applications to gene network reconstruction, eQTL mapping, and longitudinal data analysis.

• Education:
  • BS in Computer Engineering, Seoul National University (2001)
  • PhD in Computer Science, University of California, Irvine (2007)
  • Postdoctoral Fellow in Computer Science and Machine Learning, Carnegie Mellon University (2010)

Her lab develops computational tools for analyzing complex genomic datasets, including methods for:

• Learning gene networks under SNP perturbations
• Allele-specific expression quantification via kallisto extensions
• Integrating multi-omics data with scalable algorithms
• Doubly mixed-effects Gaussian process regression for spatio-temporal modeling
• Joint covariance estimation in high-dimensional biological datasets

Recent work demonstrates methodological advancements in handling dependencies among samples and features in genomic studies. She teaches EPIDEM 2186 Introduction to R Programming within the epidemiology curriculum.