Professor John Thangarajah's passion is in innovation and leadership.
He is a Professor in Artificial Intelligence and the Associate Dean (Head) of Computer Science and Software Engineering at RMIT University.
His interests are in Agent Oriented Software Development (how do we build and construct Intelligent Systems), Agent Reasoning (how can programs behave in smart ways), Intelligent Conversation Systems (how can systems interact intelligently satisfying a user's information needs), Agent Testing (providing assurance that the systems work) and in Intelligent Games (how can intelligent game masters be introduced into role playing games).
He collaborates with industry partners and researchers both local and international and have secured research grants from both government and industry sectors. He also has many years experience in teaching IT related courses to undergraduate, postgraduate and industry personnel.
Professor John Thangarajah's goal is to continue to be involved in developing smart systems that are not only intellectually challenging but also have practical benefit and impact.
Associate Professor John Thangarajah has taught and continues to teach a variety of courses, both undergraduate and postgraduate ranging from general Programming courses to more specialised Artificial Intelligence courses.
- PhD. Computer Science, RMIT University, 2005
- Yao, Y.,Alechina, N.,Logan, B.,Thangarajah, J. (2021). Intention Progression under Uncertainty In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, 7-15 January 2021
- Rodriguez, S.,Thangarajah, J.,Winikoff, M. (2021). User and System Stories: An Agile Approach for Managing Requirements in AOSE In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, London, UK (Virtual), 3-7 May 2021
- Korevaar, S.,Tennakoon, R.,Page, M.,Brotchie, P.,Thangarajah, J.,Florescu, C.,Sutherland, T.,Kam, N.,Bab-Hadiashar, A. (2021). Incidental detection of prostate cancer with computed tomography scans In: Scientific Reports, 11, 1 - 10
- Vukovic, M.,Cavedon, L.,Thangarajah, J.,Rodriguez, S. (2021). Performance degrades less under increased workload with the addition of speech control in a dynamic environment In: Applied Ergonomics, 96, 1 - 12
- Evertsz, R.,Thangarajah, J. (2020). A Framework for Engineering Human/Agent Teaming Systems In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, United States, 7-12 February 2020
- Dann, M.,Thangarajah, J.,Yao, Y.,Logan, B. (2020). Intention-Aware Multiagent Scheduling In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), Auckland, New Zealand, 9-13 May 2020
- Vukovic, M.,Sethu, V.,Parker, J.,Cavedon, L.,Lech, M.,Thangarajah, J. (2019). Estimating cognitive load from speech gathered in a complex real-life training exercise In: International Journal of Human-Computer Studies, 124, 116 - 133
- Evertsz, R.,Thangarajah, J.,Ly, T. (2019). Practical Modelling of Dynamic Decision Making, Springer, Cham, Switzerland
- Dann, M.,Zambetta, F.,Thangarajah, J. (2019). Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), Hawaii, United States, 27 January - 1 February 2019
- De Waal, G.,Thangarajah, J.,McMurray, A. (2019). Artificial Intelligence and Frugal Innovation: A formidable alliance in Future Education In: Management and Business Education in the Time of Artificial Intelligence, Information Age Publishing, Incorporated, Charlotte, USA
3 PhD Current Supervisions4 PhD Completions
- Alignment study of C-BDI and TDF. Funded by: DIIS - Innovations Connections - Competitive from (2021 to 2021)
- Requirements Management Framework for Autonomous Systems (RMAS) in Agile Command and Control (Administered by University of Melbourne). Funded by: Defence Science Institute Grant 2019 onwards from (2020 to 2021)
- Shared situation picture compilation exploiting next generation data links. Funded by: Defence Science and Technology Group - Competitive from (2018 to 2019)
- An Investigation into how best to present autonomous system decision making to human team members. Funded by: DST Competitive Evaluation Research Agreement (CERA) Program 2017 from (2017 to 2019)
- Introducing teaming to the intelligent Watch Dog (iWD). Funded by: Defence Science Institute Grant 2015 from (2016 to 2016)