Data from the Physical World

From greenhouses to construction sites, some of the most interesting data problems in physical AI show up in the real world. That's where data has to turn into decisions and movement. This meetup brings together Monumental, Source.ag and PyData Amsterdam for an evening of educational talks from engineers who work on these problems every day. Each talk is about 30 minutes, with plenty of room for questions. Schedule Details Who should attend? Data scientists, software engineers, Python users, and anyone curious about how data works in the physical world. About the Speakers Josefine Quack Josefine is an experienced Forward Deployed Robotics Engineer at Monumental, where she hacks and plays with robots all day. As a result those robots have already built over 100 houses. Please don't leave any chocolate around her unattended. Sebastiaan VermeulenSebastiaan Vermeulen leads harvest forecasting at Source.ag (econometrics/quant finance background, 4.5 years in). He'll unpack why predicting greenhouse harvests is a hard ML problem: sparse, messy real world data, biology pure ML gets wrong, and the mechanistic/ML hybrids Source uses to fix it. About the hosts Monumental builds autonomous robots that construct buildings. We're already laying bricks on family homes, canal walls and industrial buildings across the Netherlands! More about Atrium, our operating system for software-defined construction Open roles @ Monumental Source.ag builds AI for greenhouse growers. We turn the flood of data coming out of modern greenhouses into clear, daily decisions, so growers can produce more food with fewer resources. More about Source.ag