Research interests

  • Artificial Intelligence, Modelling and Simulation
  • Datamining and Machine Learning
  • Decision Support and Optimisation
  • Health Informatics

Personal webpage


Professor Uwe Aickelin 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 on Artificial Intelligence to the UK Research Councils and Government.

Professor Aickelin has worked for more than twenty years in the fields of Artificial Intelligence, Optimisation and Datamining. His specific expertise is in the modelling stages of problems with a focus on robust methods to overcome uncertainty. He has authored over 200 papers in leading international journals and conferences (Google citations 9000, H-index 50) 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. 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. 2019, pp. 960-964. DOI: 10.1109/TALE.2018.8615252
  2. 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.
  3. 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
  4. Fattah, P.; Aickelin, U.; Wagner, C. Measuring Player's Behaviour Change over Time in Public Goods Game. SAI Intelligent Systems Conference 2016 London. SPRINGER INTERNATIONAL PUBLISHING AG. 2018, Vol. 16, pp. 1039-1052. DOI: 10.1007/978-3-319-56991-8_81
  5. 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
  6. Fu, X.; Ch'ng, E.; Aickelin, U.; See, S. CRNN: A Joint Neural Network for Redundancy Detection. 2017 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE. 2017, pp. 1-8. DOI: 10.1109/SMARTCOMP.2017.7946996
  7. 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 (SSCI). IEEE. 2017, Vol. 2018-January. DOI: 10.1109/SSCI.2017.8280905
  8. Fattah, P.; Aickelin, U.; Wagner, C. Measuring Behavioural Change of Players in Public Goods Game. . Springer. 2017, Vol. tba, pp. tba-tba.
  9. Kabir, S.; Wagner, C.; Havens, TC.; Anderson, DT.; Aickelin, U. Novel Similarity Measure for Interval-Valued Data Based on Overlapping Ratio. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015623
  10. Siuly, S.; Huang, Z.; Aickelin, U.; Zhou, R.; Wang, H.; Zhang, Y.; Klimenko, SV. Preface. . 2017, Vol. 10594 LNCS, pp. V-VI.
  11. Aickelin, U. Robust Datamining. 4th Asia Pacific Conference on Advanced Research (APCAR- MAR 2017), Melbourne, Australia. 2017.
  12. 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 (BIBM). IEEE. 2017, Vol. 2017-January, pp. 417-424. DOI: 10.1109/BIBM.2017.8217685
  13. Navarro, J.; Wagner, C.; Aickelin, U.; Green, L.; Ashford, R. Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. 2016, pp. 2157-2164. DOI: 10.1109/FUZZ-IEEE.2016.7737959
  14. Navarro, J.; Wagner, C.; Aickelin, U.; Green, L.; Ashford, R. Measuring Agreement on Linguistic Expressions in Medical Treatment Scenarios. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2016. DOI: 10.1109/SSCI.2016.7849895
  15. 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
  16. Fattah, P.; Aickelin, U.; Wagner, C. Optimising Rule-Based Classification in Temporal Data. ZANCO Journal of Pure and Applied Sciences. 2016, Vol. 28, pp. 135-146.
  17. Reps, JM.; Aickelin, U.; Hubbard, RB. Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining. Computers in Biology and Medicine. PERGAMON-ELSEVIER SCIENCE LTD. 2016, Vol. 69, pp. 61-70. DOI: 10.1016/j.compbiomed.2015.11.014
  18. Zhang, T.; Siebers, P-O.; Aickelin, U. Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK. Technological Forecasting and Social Change. ELSEVIER SCIENCE INC. 2016, Vol. 106, pp. 74-84. DOI: 10.1016/j.techfore.2016.02.009
  19. Ma, J.; Sun, L.; Wang, H.; Zhang, Y.; Aickelin, U. Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams. ACM Transactions on Internet Technology. ASSOC COMPUTING MACHINERY. 2016, Vol. 16, Issue 1. DOI: 10.1145/2806890
  20. Reps, JM.; Garibaldi, JM.; Aickelin, U.; Gibson, JE.; Hubbard, RB. A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations. Journal of Biomedical Informatics. ACADEMIC PRESS INC ELSEVIER SCIENCE. 2015, Vol. 56, pp. 356-368. DOI: 10.1016/j.jbi.2015.06.011

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