Welcome to the Shim Lab. Our group develops statistical methods and computational tools for applications to a wide range of biological questions, including how genetic variants affect organism-level traits such as human disease susceptibility. Much of our research has focused on statistical methods for analyses of complex and large-scale genomic data, aiming to better understand the cellular-level processes underlying the link between genetic variants and important organism-level traits. Please see Research for more information on multiple projects, including:
- Multi-scale (e.g., wavelets) methods for analyses of functional data, including functional phenotypes arising from high-throughput sequencing assays.
- Genome-wide association analysis of complex traits.
- Inference of transcription factor binding and motif finding.
- Analysis of ribosome profiling data to better understand translation.
- Alternative splicing.
In particular, we prefer to develop methods and analyze data in close communication with biologists so that the key biological questions and unique data properties are properly addressed in the statistical approaches. We are also interested in cultivating a culture of interdisciplinary collaboration in our research community.
We seek highly motivated students to join our group. We are based in the Department of Statistics at the Purdue University. Our research interests include Bayesian statistics, computational statistics, and functional data analysis. Please feel free to contact Heejung Shim if you are interested in joining us or having a rotation.