As a graduate student in the Califano Lab, my research has focused on improving methods for understanding drug mechanism of action (MoA). Identifying drug MoA is critical for the development of new drugs, for understanding the mechanisms behind the occurrence of drug side effects, and for repositioning existing drugs. I developed an algorithm, DeMAND (Detecting Mode of Action Using Network Dysregulation), which uses a systems biology approach to determine a drug mode of action based on gene expression microarray data. I have also been involved in a project focusing on neuroendocrine tumor sequencing and analysis. In particular, I have focused on developing a computational analysis pipeline for reducing false positive somatic SNV calls from FFPE samples and interrogating tumor clonal evolution.

Before joining the Califano Lab, I was involved in a variety of clinical research projects as a microarray data analyst at Seoul National University Hospital. I then moved to the Bioinformatics Research Center at MACROGEN, Inc. The company collaborated with major Korean hospitals to develop genome-based prognostic or diagnostic kits. I utilized numerous genomic platforms, such as SNP chips, mRNA expression chips, array CGH, and methylation chips to develop more robust diagnostic and prognostic kits.