Baltimore Air Quality Dashboard

As a primary developer, co-lead a Johns Hopkins Data Science and AI Institute engineering team to build the Baltimore AQ dashboard.

Integrate multiple low-cost sensor networks, calibrate them against one regulatory air monitor using a GP based Bayesian spatial model with quantified uncertainty.

Serve high-resolution maps and summary visualizations, delivering the first spatially resolved air quality checker for Baltimore.

Now transitioning from historical display to near-real-time operation.