Research interests

  • Artificial Intelligence (Modelling and Simulation)
  • Data Mining and Machine Learning (Robustness and Uncertainty)
  • Decision Support and Optimisation (Medicine and Digital Economy)
  • Health Informatics (Electronic Healthcare Records)

Personal webpage


Professor Aickelin has worked for more than twenty years in the fields of Artificial Intelligence, Optimisation and Data Mining and is the Head of School of Computing and Information Systems at the University of Melbourne. Prior to this role, he was Vice-President at the University of Nottingham Ningbo China and Head of School of Computer Science at the University of Nottingham. He also served for many years as a strategic adviser for Artificial Intelligence to the UK Research Councils and Government. Here are my latest thoughts on AI and what it means for the workforce of the future.

His specific expertise is in the modelling stages of biomedical problems with a focus on robust methods to overcome uncertainty. Typical application areas of his work are Decision Support and Optimisation in Health Informatics. He has authored over 200 papers in leading international journals and conferences (Google citations 10000+, H-index 53) and participated in over 100 international conference programme committees. Since 2007 he has been an associate editor of the leading international journal in his field (IEEE Transactions on Evolutionary Computation).

His YouTube videos have been watched by more than 600,000 people:
Anti-Learning - So bad it’s good
How GCHQ classifies computer security
Machine Learning Methods
Nuggets of Data Gold
The Known Unknowns
Why missing data is the best
Artificial Intelligence - the code for consciousness

Recent publications

  1. Khorshidi, HA.; Haffari, G.; Aickelin, U.; Hassani-mahmooei, B. Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering.. Studies in Health Technology and Informatics. IOS Press. 2019, Vol. 266, pp. 1-6. DOI: 10.3233/SHTI190764
  2. Wu, X.; Akbarzadeh Khorshidi, H.; Aickelin, U.; Edib, Z.; Peate, M. Imputation techniques on missing values in breast cancer treatment and fertility data.. Health Information Science and Systems. BioMed Central. 2019, Vol. 7, Issue 1, pp. 19-19. DOI: 10.1007/s13755-019-0082-4
  3. Khorshidi, HA.; Aickelin, U.; Haffari, G.; Hassani-mahmooei, B. Multi-objective semi-supervised clustering to identify health service patterns for injured patients. Health Information Science and Systems. SPRINGER. 2019, Vol. 7, Issue 1. DOI: 10.1007/s13755-019-0080-6
  4. Khorshidi, HA.; Marembo, M.; Aickelin, U. Predictors of Return to Work for Occupational Rehabilitation Users in Work-Related Injury Insurance Claims: Insights from Mental Health. Journal of Occupational Rehabilitation. Springer Verlag. 2019, Vol. 29, Issue 4, pp. 740-753. DOI: 10.1007/s10926-019-09835-4
  5. Li, X.; Fu, X.; Lu, Z.; Bai, R.; Aickelin, U.; Ge, P.; Liu, G. Retrieving and ranking short medical questions with two stages neural matching model. 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE. 2019, pp. 873-879. DOI: 10.1109/CEC.2019.8790326
  6. Dent, I.; Craig, T.; Aickelin, U.; Rodden, T. A Method for Evaluating Options for Motif Detection in Electricity Meter Data. Journal of Data Science. Columbia University, New York. 2018, Vol. 16, Issue 1, pp. 1-28.
  7. Fattah, P.; Aickelin, U.; Wagner, C. Measuring behavioural change of players in public goods game. Studies in Computational Intelligence. Springer International Publishing. 2018, Vol. 751, pp. 242-263. DOI: 10.1007/978-3-319-69266-1_12
  8. Fattah, P.; Aickelin, U.; Wagner, C. Measuring Player’s Behaviour Change over Time in Public Goods Game. PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 2. Springer. 2018, Vol. 2, pp. 1039-1052. DOI: 10.1007/978-3-319-56991-8_81
  9. Roadknight, C.; Rattadilok, P.; Aickelin, U. Teaching Key Machine Learning Principles Using Anti-learning Datasets. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE. 2018, pp. 960-963. DOI: 10.1109/TALE.2018.8615252
  10. Aickelin, U.; Reps, JM.; Siebers, P-O.; Li, P. Using simulation to incorporate dynamic criteria into multiple criteria decision-making. Journal of the Operational Research Society. TAYLOR & FRANCIS LTD. 2018, Vol. 69, Issue 7, pp. 1021-1032. DOI: 10.1080/01605682.2017.1410010
  11. Fu, X.; Ch'ng, E.; Aickelin, U.; See, S. CRNN: A Joint Neural Network for Redundancy Detection. 3rd IEEE international conference on smart computing (Smartcomp 2017). IEEE. 2017, pp. 1-8. DOI: 10.1109/SMARTCOMP.2017.7946996
  12. Jiang, X.; Bai, R.; Landa-silva, D.; Aickelin, U. Fuzzy C-Means-based Scenario Bundling for Stochastic Service Network Design. 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Honolulu, Hawaii, USA. IEEE. 2017, Vol. 2018-January. DOI: 10.1109/SSCI.2017.8280905
  13. Fattah, P.; Aickelin, U.; Wagner, C. Measuring Behavioural Change of Players in Public Goods Game. . Springer. 2017, Vol. tba, pp. tba-tba.
  14. Kabir, S.; Wagner, C.; Havens, TC.; Anderson, DT.; Aickelin, U. Novel Similarity Measure for Interval-Valued Data Based on Overlapping Ratio. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017). IEEE. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015623
  15. Siuly, S.; Huang, Z.; Aickelin, U.; Zhou, R.; Wang, H.; Zhang, Y.; Klimenko, SV. Preface. . 2017, Vol. 10594 LNCS, pp. V-VI.
  16. Aickelin, U. Robust Datamining. 4th Asia Pacific Conference on Advanced Research (APCAR- MAR 2017), Melbourne, Australia. 2017.
  17. Ruan, C.; Wang, Y.; Zhang, Y.; Ma, J.; Chen, H.; Aickelin, U.; Zhu, S.; Zhang, T. THCluster:Herb Supplements Categorization for Precision Traditional Chinese Medicine. 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA. IEEE. 2017, Vol. 2017-January, pp. 417-424. DOI: 10.1109/BIBM.2017.8217685
  18. Navarro, J.; Wagner, C.; Aickelin, U.; Green, L.; Ashford, R. Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), 24-29 July 2016, Vancouver, Canada. IEEE. 2016, pp. 2157-2164. DOI: 10.1109/FUZZ-IEEE.2016.7737959
  19. Navarro, J.; Wagner, C.; Aickelin, U.; Green, L.; Ashford, R. Measuring Agreement on Linguistic Expressions in Medical Treatment Scenarios. 2016 IEEE Symposium on Computational Intelligence, 6-9 Dec 2016, Athens, Greece.. IEEE. 2016. DOI: 10.1109/SSCI.2016.7849895
  20. Miller, S.; Wagner, C.; Aickelin, U.; Garibaldi, JM. Modelling cyber-security experts' decision making processes using aggregation operators. Computers & Security. ELSEVIER ADVANCED TECHNOLOGY. 2016, Vol. 62, pp. 229-245. DOI: 10.1016/j.cose.2016.08.001

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile