Professor Jason Ford

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Professor in Electrical Engineering, School of Electrical Engineering and Robotics

Doctor of Philosophy (Australian National University), Bachelor of Engineering (Australian National University), Bachelor of Science (Australian National University)

About Jason:

Jason creates decision systems for dynamic systems, including robotic and autonomous systems, that can reliably operate in the presence of uncertainty and error. Jason has over 20 years of experience developing solutions for energy, aerospace and defence industries. He has led the development of several aerial autonomous system technologies including general aviation aircraft flight control systems which are operating in Australian, European and US airspaces.

Jason’s technical expertise is in the area of information-theoretic and optimisation approaches to model-based filtering, estimation, detection, decision and planning for dynamic systems and their related inverse problems. With this expertise, he is striving to understand the role of models in knowledge so that he can design more capable technology. His current research interests include aerial platform autonomy for infrastructure inspection and low signal-to-noise ratio anomalous signal detection with application in aerospace and other domains.

Academic and Professional Experience:

Dr Jason J. Ford is a Professor at the Queensland University of Technology (QUT). He graduated from the Australian National University (ANU) with the B.Sc. and B.E. degrees in 1995, and a PhD degree in 1998. In 1998 Jason joined the Australian Defence Science and Technology Organisation (now called DSTG) as a Research Scientist (promoted to Senior Research Scientist in 2000). In 2004 Jason was appointed a Research Fellow at the University of New South Wales at the Australian Defence Force Academy.  In 2005 Jason joined the QUT as a Research Fellow, before appointment as Lecturer in Electrical Engineering in 2007 (promoted to Senior Lecturer in 2010, to Associate Professor in 2016 and to full Professor in 2019).

Research and Industry Impact Highlights:

  • The 2019 Academic of the Year Award, Australian Defence Industry Awards.
  • Development and commercialisation of aircraft automation systems for infrastructure inspection within the ROAMES asset management system. Savings to the state of Queensland (alone) are estimated to exceed $40M/year, but ROAMES also operates in the US and UK. The impact of this rare example of an Australian developed aircraft flight technology has been acknowledged via:
    • the Australian Research Council’s 2018 Engagement and Impact exercise assessed the aircraft flight technology as having high impact (their highest rating).
    • the ROAMES system winning a UK Network Game changer Award, a US International Edison Award, a Queensland Spatial Excellence Awards, JM (Mac) Seriser Award.
  • More than a decade of sustained research activity on the extremely challenging problem of replicating the human pilot vision system to create a vision based sense and avoid technology for autonomous aerial systems. The impact of this technology has been acknowledged via collaboration project awards:
    • a Queensland iAward,
    • a B-HERT award, and
    • an Engineering Australia Excellence Award (Queensland Division).

Career Summary:

  • Co-authored more than 100 peer reviewed research publications: Google Scholar, QUT E-prints, ORCID, and/or Publons.
  • Patented flight plan and flight control technology: Method and apparatus for developing a flight path. Inventors: Troy Bruggemann and Jason Ford, Patent details can be found via the following patent numbers – Australia: 2014360672, United States: 9983584, Canada: CA2969552, Europe: EP3077881.
  • Attracted over $10 million dollars in competitive research funds within Australia since 2009.
  • Currently a chief investigator in QUT’s Centre of Robotics (Program Lead – Decision and Control) and an Associate Investigator in the ARC Centre of Excellence for Robotic Vision and QUT’s Centre of Data Science.
  • 8 HDR student completions since 2008.
  • Taught Control System Engineering and Autonomous Systems to more than 1000 undergraduate electrical, aerospace and mechatronic engineers.

Additional information

Type
Academic Honours, Prestigious Awards or Prizes
Reference year
2019
Details
The 2019 Academic of the Year Award, Australian Defence Industry Awards.
Title
Automated vision-based aircraft collision warning technologies
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
LP100100302
Start year
2010
Keywords
Collision Warning; Aerial robotics; National Airspace