I am a postdoctoral research scientist in Dr. Califano lab at Columbia University. My research is focused on computational biology that combines statistical models and machine learning techniques to answer a wide range of problems in biology. I have a background in chemistry, but also went through formal training in statistics, computer science and mathematics during my PhD study by taking over forty courses (with credits). My research during graduate school is mainly about computational proteomics. I developed a novel method/software to predict protein-protein interactions in native cellular environment using mass spectrometry data. Subsequently, I applied this technique to a widely used model organism (Caenorhabditis elegans) to chart the first biochemically derived protein-protein map for nematode species. My postdoctoral training at Columbia University mainly lays in the field of systems pharmacology. To integrate my expertise of chemistry and quantitative analysis, I am developing computational models to predict mechanisms of actions of drugs and answer many other important questions related to biology and pharmacology. I have published papers on several high-impact journals including a first author paper in Nature Methods. Some key discoveries for my work here in Columbia will soon be summarized to several papers and submitted to high-impact journals. As a result, I am planning to start my independent research career after postdoctoral training in the future.