Simran Dave
Simran is a PhD student in high energy physics at University College London, working on direct searches for dark matter with the LUX-ZEPLIN experiment. She is undertaking a data science placement at Nesta, working with the sustainable future team to map the local heat transition using open data. She holds a Master's degree in Theoretical Physics from Imperial College London.
Session
Decarbonising UK’s home heating is one of the greatest challenges of the Net Zero transition, yet it currently relies on individual household decisions supported by government incentives. To help accelerate the local delivery, we are building a tool that maps the most suitable low-carbon heating for clusters of properties at a neighbourhood level.
In this talk we will walk through our end-to-end data science pipeline, covering processing of large-scale geospatial data, the nuances of modelling where ground truth data does not yet exist, and how to translated local authorities needs into a functional product. We will present our Python tech stack and will conclude with a showcase of the user interface.
Whether you're interested in geospatial data engineering, machine learning for social good, or how to work within a multidisciplinary team, this talk offers a blueprint for building data products with real-world impact.