Prof Will Browne

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Engineering Doctorate (University of Wales)

Professor Will Browne’s expertise is in Artificial Cognitive Systems with over 27 years of experience in Artificial Intelligence and Robotics. The ‘Professor and Chair in Manufacturing Robotics’ title is through QUT, the ARM Hub and CSIRO who have jointly invested in this position. This is to further University and Industry collaboration in the development of advanced robotics in a wide range of manufacturing, from aquaculture to factory operations to healthcare.

The ARM Hub is an independent not-for-profit agile technology application centre for robotics, artificial intelligence and design-led manufacturing. It is an impactful aggregator of research and development, linking private industry, cutting-edge research and Government to uplift, upskill and transform Australian manufacturing.  It draws together skilled teams of scientists, technical specialists, designers, and engineers, to develop commercial, advanced manufacturing solutions.

He is co-PI of the $5 million SfTI Robotics Spearhead developing the science necessary for human robot collaboration.
Will is leading an Industrial Transformation Research Hub proposal on Flexible, Intelligent, Robust Manufacturing through the use of artificial intelligence for advancing manufacturing, e.g. effective construction and use of digital twins.

International links include being the NZ representative on the Australian Robotics and Automation Association council.

Will is recognised internationally in the field of Learning Classifier Systems (LCS), being elected by his peers to co-organise the International Workshop on Learning Classifier Systems, invited to serve as co-track chair in the field’s major conference GECCO (Genetic and Evolutionary Computation Conference) 2011, 2012, 2017 & 2018 and serve on Journal editorial boards. He has presented, with international colleagues, multiple introductory tutorials on LCS at international conferences, e.g. Congress on Evolutionary Computation, GECCO and World Congress on Computational Intelligence. He is presenting the first tutorial on advanced LCS in GECCO 2021. Together with Dr Ryan Urbanowicz (University of Pennsylvania, USA) he co-authored the first textbook on LCS. He has 24 years’ experience in developing the LCS approach, which as a transparent symbolic learning system is regaining popularity due the need for Explainable AI (XAI).

Additional information

2021 –                   School of Electrical Engineering & Robotics, QUT


2009 – 2021         School of Engineering and Computer Science, Victoria University of Wellington.

2001 to 2009       Lecturer, Cybernetics, School of Systems Engineering, University of Reading, UK.

1998 to 2001        Post-Doctoral Research Associate in the Control and

Instrumentation Research Group, University of Leicester, UK.

1994 to 1998         Eng.D,  University of Wales, Cardiff and British Steel, through the Engineering Doctorate scheme, South Wales.

“The development of an industrial learning classifier system for application to a steel hot strip mill”

1993 to 1994          MSc in Energy (Distinction), University of Wales, Cardiff.

1990 to 1993         B. Eng. Mechanical Engineering, Honours, University of Bath, UK