Core Infra

Geospatial Data Monitoring Platform

• Worked as a core software engineer in a geospatial big data company, which constructs data pipelines to process satellite images, and leverages artificial intelligence to get some insights on a global scale.

• Designed and built a data monitoring platform, where data monitors and alerts can be visualized, configured, and set up along the geospatial data pipeline to improve data reliability and quality.

• Upgraded database schema on PostgreSQL for time-series data storage, set up alert rules based on historical data analytics using Grafana, and realized the monitoring of image count, delivery delay, and ingestion delay.

• Dockerized the data monitors, streamlined the deployment and management of Kubernetes applications using helm, and automated the whole process with continuous integration(CI/CD) on Jenkins.

• Deployed the monitoring platform into production, and reduced the time delay of anomaly discovery by 70%.

Big Data Swimming-bot for Pipeline Leak Detection

• Collaborated with a team of 4 and designed an in-pipe low-power usage leak detecting swimming robot based on Arduino 101 and sensors(gyroscope and microphone).

• Implemented several classification models(e.g. SVM, Bayesian, NN) and achieved leak detection accuracy of 90%, which helped Enbridge(North America leading energy delivery company) save millions of dollars in labor cost.

• Published paper on American Control Conference (Jun 27-29,2018, Milwaukee, WI) as first author.