Biography

Li Chen
Assistant Professor of Medicine
Member, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
410 W. 10th Street, HS 5025
Indianapolis, IN 46202
USA

Email: chen61@iu.edu

I am a tenure-track Assistant Professor of Medicine and a member in Center for Computational Biology and Bioinformatics at Indiana University School of Medicine (IUSM). Before joining IUSM, I spent two years as an Assistant Professor in Harrison School of Pharmacy at Auburn University. I received a BS from China majoring in Bioinformatics in 2009; a MS in Biostatistics and another MS in Computer Science from the Johns Hopkins University in 2011 respectively. After that, I obtained my PhD in Computer Science and Informatics at Emory University in 2017. I also interned in CareerBuilder Data Science and Amazon Machine Learning in Summer 2014 and 2015 respectively.

Research

I am interested in data science research with its application in large-scale omics data. I am leading a small interdisciplinary data science group working on bioinformatics & biostatistics projects. My current research focuses on developing statistical and informatics methods for analyzing of large-scale omics data. Projects include but not limited to

• Epigenomics: Develop statistical methods and database for analyzing epigenomic data
  • Differential binding. [Link to ChIPComp]
  • Database and web server. [Link to hmChIP]
• Genetics: Develop computational methods for annotating and analyzing functional variants (GWAS SNPs, cis-eQTL SNVs) by utilizing large-scale multi-omics profiles
  • GWAS variant enrichment analysis. [Link to traseR]
  • GWAS variant prediction. [Link to DIVAN]
  • cis-eQTL variant prediction. [Link to TIVAN]
• Metagenomics: Develop statistical methods for analyzing microbiome data
  • Normalization. [Link to GMPR]
  • Predictive modeling using Graph-Constraint Sparse Regression Model. [Link to glmgraph]
  • Predictive modeling using Graph-Constraint Sparse Regression Model with encouraging local smoothing. [Link to SICS]
  • Predictive modeling using Generalized Linear Mixed Model. [Link to glmmTree]
• Single-cell genomics: Develop statistical methods for clustering single-cell data
  • Clustering droplet-based single-cell transcriptomic data from population studies. [Link to BAMMSC]
• Transcriptomics: Develop statistical methods for analyzing non-coding RNAs
  • Genomic feature annotation and differential expression analysis of circular RNAs. [Link to circMeta]