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

http://aickelin.com

Biography

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 for 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 Data Mining. His specific expertise is in the modelling stages of 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 9500, 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. Akbarzadeh Khorshidi, H.; 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 Nature. 2019. DOI: 10.1007/s10926-019-09835-4
  2. 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
  3. 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.
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. Fattah, P.; Aickelin, U.; Wagner, C. Measuring Behavioural Change of Players in Public Goods Game. . Springer. 2017, Vol. tba, pp. tba-tba.
  10. 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
  11. Siuly, S.; Huang, Z.; Aickelin, U.; Zhou, R.; Wang, H.; Zhang, Y.; Klimenko, SV. Preface. . 2017, Vol. 10594 LNCS, pp. V-VI.
  12. Aickelin, U. Robust Datamining. 4th Asia Pacific Conference on Advanced Research (APCAR- MAR 2017), Melbourne, Australia. 2017.
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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.
  18. 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
  19. 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
  20. Ma, J.; Sun, L.; Wang, H.; Zhang, Y.; Aickelin, U. Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams. ACM Transactions on Internet Technology (TOIT). ASSOC COMPUTING MACHINERY. 2016, Vol. 16, Issue 1. DOI: 10.1145/2806890

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