Optimizing EV Charging Stations: A Live Code-Along in Zerve
Join Greg Michaelson and Jason Hillary for a fully interactive, hands-on code-along where you’ll build an end-to-end optimization project from scratch using Zerve’s new notebook view. You’ll write code with us step by step, ask questions as you go, and learn how to model a synthetic city, generate EV charging demand, define an optimization objective, and use heuristic search to place charging stations efficiently.
This session is designed for active participation: follow along, experiment, and get real-time guidance from two experienced PhD data scientists. Everyone who attends gets free Zerve credits so you can continue exploring and applying these techniques long after the workshop.
Key takeaways from attending this event:
How to translate a real-world planning problem into an optimization objective (for example, constructing a weighted distance function and defining constraints like service radius.)
How to implement and iterate on heuristic optimization methods (such as k-medoids–style search) to improve facility placement without relying on packaged solvers.
How to build and visualize an end-to-end optimization workflow in Zerve, including generating synthetic spatial data, evaluating solutions, and comparing baseline vs optimized layouts.
You MUST sign up on the Data Science Festival site to participate! https://datasciencefestival.com/session/optimizing-ev-charging-stations-a-live-code-along-in-zerve/