About

Lily Xu is a highly skilled and experienced software engineer with a passion for building data-centric products. Currently working at Stripe, Lily has a proven track record of success in her field, having worked for over 12 years in various roles as a full stack developer, team lead, and software engineer. Lily's expertise lies in her ability to leverage data in beautiful and interesting ways to build innovative products that solve real-world problems. Her experience includes working with a range of technologies, including Python, JavaScript, AWS, Cassandra, and Django, among others. In her most recent role at U.S. Digital Response, Lily developed a full-stack interactive web app that visualizes different policy intervention scenarios based on models developed by the Johns Hopkins' epidemiology team to support public health agencies in making decisions for their state's emergency response. She built the front-end in React and D3, which visualizes 300 GB of simulation data, and the back-end web scripts in Python and Node to analyze and transform Parquet data. The app is live at www.iddynamics.jhsph.edu/covid/dashboard (hosted on S3). Lily's previous experience includes working as a full-stack developer and team lead at Verdigris, where she built a full-stack analytics web platform, the core product offering using Node Express backend backed by Cassandra, and a frontend built on Vue.js, D3, and Bootstrap. She also built an energy anomaly detection system that led to the largest deal and partnership in company history. She forecasted customer energy with Generalized Additive Model (GAM) in Python on AWS Lambda, which detected leaks and malfunctions in physical customer warehouses. Lily holds a Bachelor's Degree in Chemical-Biological Engineering from the Massachusetts Institute of Technology and a Master's Degree in Environmental and Water Resources Engineering from the University of Texas at Austin. With her extensive experience and expertise in software engineering, Lily is a valuable asset to any team working on data-centric products.