ÐÇ¿Õ´«Ã½

Dr. Song has a broad background in biology, computer science, epidemiology, and biostatistics, with specific training and expertise in statistical methodology, hands-on software development, bioinformatics, and the application of numerous computational statistical techniques to genetics, genetic epidemiology, and genomic data. She also has extensive experience in implementing existing and/or creating unique computational tools utilizing advanced statistical methodologies for use by the research community. 

Dr. Song has been instrumental in many of the research projects undertaken by her colleagues in the Department,  across the ÐÇ¿Õ´«Ã½ School of Medicine and external institutes and has developed novel programs to address complex research inquiries. For more than 15 years, she has worked as an analyst/programmer on university-funded and NIH-funded grants, designing, coding, and maintaining the computer software package S.A.G.E. (Statistical Analysis for Genetic Epidemiology ). She also has developed several software tools including a web-based tool for pedigree informatics (PedWiz ), an R package for family-based structural equation modeling (strum) and a tool for family-based big data analysis (ONETOOL ). 

Several faculty members work with her to manage big data computational resources and databases, including Progeny for family data, for various national and international research consortia. She has collaborated with numerous researchers and performed computational and statistical data analyses for many projects, including GWAS, imputation, meta-analysis, and sequencing data analyses leading to several peer-reviewed publications. 
 
Find Dr. Song's publications through several sources: