<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/bb90e1367cd747a5b92abc6a9086113c&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/bb90e1367cd747a5b92abc6a9086113c-0e0c6b022ce29eac.gif</thumbnail_url><duration>106.888</duration><title>Optimizing EV Charging Stations in Austin 🚗</title><description>In this video, I introduced Charge Pilot, a tool we&apos;ve developed to analyze the EV station landscape in Austin, Texas. We identified which stations are underserved and overbuilt, and used a neural network to recommend optimal locations for new stations based on traffic data and population maps. Our model provides an efficiency score from 0 to 100, helping retailers and EV suppliers optimize their resources and serve communities better. I encourage you to explore our findings and consider how they can impact your business decisions in the EV market.</description></oembed>