Worked in Shopping Insights project at Google. Shopping Insights helps retailer understand what shoppers want. It graphically displays what products and brands are popular, trending up or down, and how they vary by region with using time series analysis. The shopping Insights data contain over 55,000 products and 45000 brands in 5000 categories across the US. Allowing subscribers to create up to 10 weekly and monthly email reports for the products and categories they are interested in. • Discover and present data findings to other scientists, designers, and product managers at various levels. • Design and deploy ETL pipeline with Python and SQL to translate complex data into deeper insights report. • Create visualization tools and dashboards for quality monitoring. • Build and deploy Datastore and application on Google Cloud Platform.

• Analyzed the risk of disease and prescription drug using quantitative research and statistical inference from large datasets (200G). • Identified the correlations between pairs of disease and gene using Hadoop spark technology (10 times faster than sequential program). • Construction of a visual disease network and a interactive literature search system using natural language processing (including text data of 400,000 PubMed abstracts article). • Discovery, assessment of novel data sources to improve predictive capabilities. • Performed data cleaning/analysis/mining using Python and R. • Implemented machine learning and deep learning strategies to build data models. • Led a research team with 5+ graduate students, provides advice and monitors performance.