Predicting Urban Land Use

Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.

Guest

Daniel Omeiza: Daniel is a postdoctoral research assistant at the Oxford Robotics Institute (ORI) and the Responsible Technology Institute, where he applies both quantitative and qualitative approaches in exploring explainability in robotic systems. He received a DPhil degree in computer science from the University of Oxford in the Human-Centred Computing group in CS department and the Cognitive Robotics group at the Oxford Robotics Institute. During his PhD, he leveraged explainable AI and behavioural science theories to develop interpretable techniques that generate intelligible explanations for automated driving decisions. These explanation techniques have been deployed within large projects like the SAX and the RAILS projects. A prototype was exhibited at the Goodwood Festival of Speed. His PhD research was generously funded by the EPSRC RoboTIPS project.