I am a research scientist in Prof. David Gifford's Computational Genomics Group in the MIT Computer Science and Artificial Intelligence Laboratory (MIT CSAIL).

As a computational biologist, my research interest is to build and apply computational models and algorithms to study gene regulation and its effects on health and disease, using machinie learning techniques and high throughput genomic data. Currently I am working on discovering regulatory enhancers and their target genes from high throughput sequencing data such as ChIP-Seq, ATAC-Seq, ChIA-PET and RNA-Seq, learninging models of the genome regulatory grammars, long-distance regulation in 3D and testing them experimentally to improve our understanding. I am also interested in applying what had been learned from regulatory genomics to interpret the functional role of non-coding genetic variations in personal genomes.

My list of publications can be found in Google Scholar.

I developed the following computational methods (with free Java softwares) for analyzing transcription factor binding and gene expression data:

• KSM motif representation and KMAC motif discovery ( web site paper)
• RMD for studying combinatorial TF binding based on probabilistic topic model ( web site paper)
• GEM for ChIP-Seq binding event finding and motif discovery ( web site paper)
• GPS for ChIP-Seq binding event finding using the "peak shape" information ( web site paper)
• GEDI for gene expression data visualization and analysis using Self-Organizing Map ( web site paper), co-developed with Gabriel Eichler.

I am originally from Guangdong, China. I received my Ph.D. from the Computational and Systems Biology PhD Program at MIT in 2012. And I received my undergraduate degree from Tsinghua University in Beijing, China.