QUT - Academic profiles - Dr Rune Rasmussen

Biography

Dr Rune Rasmussen joined QUT in 2017 as Senior Software Engineer for the Research Engineering Facility. Rune's interests are in solving and finding high-performing software solutions to problems in complex systems, which, in his current role, concerns solving software problems in robotics and autonomous systems. Rune has programming skills applicable in small embedded systems, through to distributed network systems, over all major operating systems, with program language skills in Assembly language, C/C++, C#, Java, Javascript, PHP, HTML, SQL, R, Matlab. Rune's current role allows him to specialise in systems involving the Robotic Operating System (ROS).

Prior to his current role, Rune worked on solving and creating artificial players of board games, creating 3D virtual environments for interactive nurse training, research and development of Monte Carlo statistical methods for solving the expansion parameters of invasive species, for solving optimal eradication programs, and for solving problems in software variant analysis and service refactoring. Rune worked as a lecturer for 7 years, where he developed, coordinated and taught the unit INB383, AI for Games.

0467 588 947

r.rasmussen@qut.edu.au

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Personal details

Positions

Senior Software Engineer
Academic Division,
Research Portfolio,
Research Engineering Facility (REF)

Research field

Artificial intelligence, Information systems

Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020

Qualifications

Doctor of Philosophy (Queensland University of Technology)

Publications

Rasmussen, R. & Hamilton, G. (2012). An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata. Environmental Modelling and Software, 29(1), 1–10. https://eprints.qut.edu.au/47105

QUT ePrints

For more publications by Rune, explore their research in QUT ePrints (our digital repository).

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2019

Hou, L., Chen, X., Lan, K., Rasmussen, R. & Roberts, J. (2019). Volumetric next best view by 3D occupancy mapping using Markov Chain Gibbs sampler for precise manufacturing. IEEE Access, 7, 121949–121960. https://eprints.qut.edu.au/231317

2018

Kelly, N., Russell, N., Kickbusch, S., Barros, A., Dawes, L. & Rasmussen, R. (2018). Online communities of teachers to support situational knowledge: A design-based study. Australasian Journal of Educational Technology, 34(5), 150–166. https://eprints.qut.edu.au/120372
Rasmussen, R., Barros, A. & Wei, F. (2018). A likelihood-free Bayesian derivation method for service variants. Journal of Systems and Software, 143, 87–99. https://eprints.qut.edu.au/118268

2017

Wei, F., Barros, A., Rasmussen, R. & De Alwis, A. A. C. (2017). Efficient service variant analysis with Markov updates in Monte Carlo tree search (Short Paper). On the Move to Meaningful Internet Systems - OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Proceedings, Part I (Lecture Notes in Computer Science, Volume 10573), 639–647. Springer. https://eprints.qut.edu.au/116198

2014

Hamilton, G., Rasmussen, R., Mullerova, J., Pergl, J. & Pysek, P. (2014). Defining the spatiotemporal surveillance space for alien species' invasions using approximate Bayesian computation. Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), Volume 3, 1347–1352. International Environmental Modelling and Software Society. https://eprints.qut.edu.au/74827

2013

Guo, H., Brown, R. & Rasmussen, R. (2013). Workflow resource pattern modelling and visualization. Computer Science 2013: Proceedings of the Thirty-Sixth Australasian Computer Science Conference [Conferences in Research and Practice in Information Technology, Volume 135], 31–40. Australian Computer Society. https://eprints.qut.edu.au/54045
Guo, H., Brown, R. & Rasmussen, R. (2013). A theoretical basis for using virtual worlds as a personalised process visualisation approach. Advanced Information Systems Engineering Workshops: CAiSE 2013 International Workshops Proceedings [Lecture Notes in Business Information Processing, 148], 229–240. Springer. https://eprints.qut.edu.au/58569
Guo, H., Brown, R. & Rasmussen, R. (2013). Human resource behaviour simulation in business processes. Information systems development: Reflections, challenges and new directions, 167–178. Springer. https://eprints.qut.edu.au/42112
Recker, J., Brown, R., Rasmussen, R., Poppe, E. & Guo, H. (2013). The BPMVE Wunderkammer. Business Process Trends. https://eprints.qut.edu.au/57800
Guo, H., Brown, R. & Rasmussen, R. (2013). 3D process visualisation configurations [Digital or Visual Product]. YouTube. https://eprints.qut.edu.au/57643
Brown, R., Johnson, D., Rasmussen, R., Recker, J. C., Poppe, E. & Guo, H. (2013). Methods for Process Modelling Simulations [Portfolio].

2012

Guo, H., Brown, R. & Rasmussen, R. (2012). Virtual worlds as a model-view approach to the communication of business processes models. Proceedings of the CAiSE'12 Forum at the 24th International Conference on Advanced Information Systems Engineering (CAiSE) [CEUR Workshop Proceedings, Volume 855], 66–73. Sun SITE Central Europe. https://eprints.qut.edu.au/48902
Rasmussen, R. & Hamilton, G. (2012). An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata. Environmental Modelling and Software, 29(1), 1–10. https://eprints.qut.edu.au/47105
Rasmussen, R. & Brown, R. (2012). A deductive system for proving workflow models from operational procedures. Future Generation Computer Systems, 28(5), 732–742. https://eprints.qut.edu.au/48181
Brown, R., Rasmussen, R., Baldwin, I. & Wyeth, P. (2012). Design and implementation of a virtual world training simulation of ICU first hour handover processes. Australian Critical Care, 25(3), 178–187. https://eprints.qut.edu.au/48523

A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/r.rasmussen