Biography
Prof. Paul Newman is the founder of the Oxford Robotics Institute, as well as the founder of Oxbotica Ltd. He is the BP Professor of Information Engineering at the University of Oxford and inventor of techniques that allow machines to know where they are, what surrounds them and what they should do next. With over 15K citations, 200 papers and fundamental contributions to robotics science he continues to extend the reach and endurance of mobile robotics – from driverless cars to Mars. He obtained an M.Eng. in Engineering Science from Oxford University, Balliol College in 1995.
He then undertook a PhD in autonomous navigation at the Australian Centre for Field Robotics, University of Sydney, Australia. In 1999 he returned to the United Kingdom to work in the commercial sub-sea navigation industry. In late 2000 he joined the Dept of Ocean Engineering at M.I.T. where as a post-doc and later a research scientist, he worked on algorithms and software for robust autonomous navigation for both land and sub-sea agents.
In early 2003 he returned to Oxford as a Departmental Lecturer in Engineering Science before being appointed to a University Lectureship in Information Engineering and becoming a Fellow of New College in 2005, Professor of Engineering Science in 2010 and BP Professor of Information Engineering and Fellow of Keble College in 2012.
He was elected Fellow of the Royal Academy of Engineering and Fellow of the IEEE in 2014 both with citations for outstanding contributions to robot navigation. He serves on the UK Department for Transport’s Scientific Advisory Council, was an architect of the UK’s Robotics and Autonomous Systems Strategy. In 2014 co-founded Oxbotica – a full stack autonomy company based in the UK which supplies autonomy software to vehicle manufactures around the world.
Contact:
Personal Assistant
Hollie Dunn
Media Enquiries
Most Recent Publications
Point-based metric and topological localisation between lidar and overhead imagery
Point-based metric and topological localisation between lidar and overhead imagery
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
Depth-SIMS: semi-parametric image and depth synthesis
Depth-SIMS: semi-parametric image and depth synthesis
What goes around: leveraging a constant-curvature motion constraint in radar odometry
What goes around: leveraging a constant-curvature motion constraint in radar odometry
Contrastive learning for unsupervised radar place recognition
Contrastive learning for unsupervised radar place recognition
