Biography
Professor Paul Goulart joined the University of Oxford in 2014 as an Associate Professor in Engineering Science and was made full Professor in September 2024. He received his SB and MSc degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology (MIT). Following his undergraduate studies he was a software developer in the flight operations centre for the Chandra X-Ray Observatory at the Harvard-Smithsonian Centre for Astrophysics, and later an engineer in the Autonomous Systems group at the Charles Stark Draper Laboratory.
In 2003, Paul was selected as a Gates Scholar at the University of Cambridge, where he received a PhD in Control Engineering in 2007. From 2007 to 2011 he was a Lecturer in control systems in the Department of Aeronautics at Imperial College London, and from 2011 to 2014 a Senior Researcher in the Automatic Control Laboratory at ETH Zurich. He is a member of the Control Group in the department of Engineering Science.
Most Recent Publications
A neural network-based approach to hybrid systems identification for control
A neural network-based approach to hybrid systems identification for control
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
FOREWORD
FOREWORD
Control of cross-directional systems with approximate symmetries
Control of cross-directional systems with approximate symmetries
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
Research Interests
Paul’s research interests are mainly in robust and high speed optimisation and control, with a wide range of application areas including fluid flows, economics and traffic networks.
Research Groups
Most Recent Publications
A neural network-based approach to hybrid systems identification for control
A neural network-based approach to hybrid systems identification for control
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
FOREWORD
FOREWORD
Control of cross-directional systems with approximate symmetries
Control of cross-directional systems with approximate symmetries
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
DPhil Opportunities
I am interested in supervising research students working in control engineering and optimisation.
Most Recent Publications
A neural network-based approach to hybrid systems identification for control
A neural network-based approach to hybrid systems identification for control
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones
FOREWORD
FOREWORD
Control of cross-directional systems with approximate symmetries
Control of cross-directional systems with approximate symmetries
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations
