Geoff Webb

Geoffrey I. Webb (also known as Geoff Webb) is Professor of Computer Science at Monash University, founder and director of Data Mining software development and consultancy company G. I. Webb and Associates,[1] and former editor-in-chief of the journal Data Mining and Knowledge Discovery.[2] Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002.

For other uses, see Geoffrey Webb (disambiguation).

Geoffrey I. Webb
CitizenshipAustralia
AwardsInaugural Eureka Prize for Excellence in Data Science, 2017
IEEE Fellow
Pacific-Asia Conference on Knowledge Discovery and Data Mining Distinguished Research Contributions Award, 2022
Australian Computer Society ICT Researcher of the Year Award 2016
IEEE International Conference on Data Mining Outstanding Service Award, 2013
Australian Research Council Outstanding Researcher Award, 2014
Scientific career
FieldsData Science
Computer Science
Artificial Intelligence
Machine Learning
Computational Biology
InstitutionsMonash University Department of Data Science and Artificial Intelligence
Notable studentsYing Yang
Jiangning Song
Chang Wei Tan

Webb has published more than 280 scientific papers in the fields of machine learning, data science, data mining, data analytics, time series analytics, big data, bioinformatics and user modeling.[3] He is an editor of the Encyclopedia of Machine Learning.[4]

Webb created the Averaged One-Dependence Estimators (AODE) machine learning algorithm[5] and its generalization Averaged N-Dependence Estimators (ANDE)[6] and has worked extensively on statistically sound association rule learning.[7][8][9] [10] His early work included advocating the use of machine learning to create black box user models;[11] interactive machine learning;[12][13] decision tree grafting;[14] and one of the first approaches to association rule learning using minimum support and confidence to find the rules for the first associative classifier, FBM.[15] He has developed multiple novel approaches to time series classification.[16][17][18] He has worked on diverse problems including concept drift,[19] scalable learning of graphical models,[20] human in the loop machine learning,[21] computational protein biology.[22]

Webb's awards include inaugural Eureka Prize for Excellence in Data Science, 2017,[23] IEEE Fellow,[24] Pacific-Asia Conference on Knowledge Discovery and Data Mining Distinguished Research Contributions Award, 2022,[25] Australian Computer Society ICT Researcher of the Year Award 2016,[26] the IEEE International Conference on Data Mining Outstanding Service Award, 2013[27] an Australian Research Council Outstanding Researcher Award, 2014[28] and multiple Australian Research Council Discovery Grants.[29] He has twice been recognised by The Australian Research Magazine as Australia's leading Bioinformatics and Computational Biology researcher.[30][31]

Webb is a foundation member of the editorial advisory board of the journal Statistical Analysis and Data Mining.[32] He has served on the Editorial Boards of the journals Machine Learning, ACM Transactions on Knowledge Discovery in Data, User Modeling and User Adapted Interaction, and Knowledge and Information Systems.

Webb was elected to the ACM Special Interest Group on Knowledge Discovery and Data Mining Executive Committee in 2017.[33]

References
  1. "G. I. Webb and Associates"
  2. "Data Mining and Knowledge Discovery Journal" Retrieved on 2013-10-20.
  3. Geoff Webb publications indexed by Google Scholar
  4. "Encyclopedia of Machine Learning"
  5. Webb, Geoffrey; J. Boughton; Z. Wang (2005). "Not So Naive Bayes: Aggregating One-Dependence Estimators". Machine Learning. 58 (1): 5–24. CiteSeerX 10.1.1.3.7847. doi:10.1007/s10994-005-4258-6. S2CID 13148847.
  6. Webb, Geoffrey; J. Boughton; F. Zheng; K.M. Ting; H. Salem (2012). "Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification". Machine Learning. 86 (2): 233–272. doi:10.1007/s10994-011-5263-6.
  7. Webb, Geoffrey (2007). "Discovering Significant Patterns". Machine Learning. 68 (1): 1–33. doi:10.1007/s10994-007-5006-x.
  8. Webb, Geoffrey (2008). "Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns". Machine Learning. 71 (2–3): 307–323. doi:10.1007/s10994-008-5046-x.
  9. Webb, Geoffrey (2010). "Self-Sufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items". Transactions on Knowledge Discovery from Data. 4: 3:1–3:20. doi:10.1145/1644873.1644876. S2CID 774593.
  10. Webb, Geoffrey (2011). "Filtered-top-k Association Discovery". WIREs Data Mining and Knowledge Discovery. 1 (3): 183–192. CiteSeerX 10.1.1.228.2541. doi:10.1002/widm.28. S2CID 14839879.
  11. Webb, Geoffrey; M. Kuzmycz (1996). "Feature based modelling: a methodology for producing coherent, consistent, dynamically changing models of agents' competencies". User Modeling and User-Adapted Interaction. 5 (2): 117–150. doi:10.1007/BF01099758. S2CID 12003265.
  12. Webb, Geoffrey (1996). "Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts". Knowledge-Based Systems. 9 (4): 253–266. CiteSeerX 10.1.1.228.3037. doi:10.1016/0950-7051(96)01033-7.
  13. Webb, Geoffrey; J. Wells; Z. Zheng (1999). "An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition". Machine Learning. 35 (1): 5–14. doi:10.1023/A:1007504102006.
  14. Webb, Geoffrey (1996). "Further Experimental Evidence Against The Utility Of Occam's Razor". Journal of Artificial Intelligence Research. 4: 397–417. doi:10.1613/jair.228. S2CID 6088084.
  15. Webb, Geoffrey (1989). "A Machine Learning Approach to Student Modelling" (PDF). Proceedings of the Third Australian Joint Conference on Artificial Intelligence (AI 89). pp. 195–205.
  16. Dempster, Angus; F. Petitjean; G. Webb (2020). "ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels". Data Mining and Knowledge Discovery. 34 (5): 1454–1495. arXiv:1910.13051. doi:10.1007/s10618-020-00701-z. S2CID 204949593.
  17. Fawaz, Hassan; B. Lucas; G. Forestier; C. Pelletier; D. Schmidt; J. Weber; G. Webb; L. Idoumghar; P. Muller; F. Petitjean (2020). "InceptionTime: Finding AlexNet for Time Series Classification". Data Mining and Knowledge Discovery. 34 (5): 1936–1962. arXiv:1909.04939. doi:10.1007/s10618-020-00710-y. S2CID 202572652.
  18. Shifaz, Ahmed; C. Pelletier; F. Petitjean; G. Webb (2020). "TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification". Data Mining and Knowledge Discovery. 34 (3): 742–775. arXiv:1906.10329. doi:10.1007/s10618-020-00679-8. S2CID 195584256.
  19. "Concept Drift: Learning From Non-Stationary Distributions"
  20. "Scalable Graphical Modeling"
  21. "Interactive machine learning and data analytics"
  22. "Computational Biology"
  23. "Eureka Prize Winners 2017"
  24. "IEEE Fellows 2015"
  25. "PAKDD 2022 Awards"
  26. ACS Digital Disruptors Awards Winners 2016
  27. ""IEEE Data Mining Awards"". Archived from the original on 18 August 2017. Retrieved 20 October 2013.
  28. Discovery Projects Funding Outcomes for Projects Commencing in 2014
  29. ""Discovery Projects Funding Outcomes"". Archived from the original on 23 October 2013. Retrieved 20 October 2013.
  30. "Deep Dive Into Research". No. 2021. The Australian. 10 November 2021. Retrieved 10 November 2022.
  31. "Our top researchers in Field Leaders Engineering & Computer Science". No. 2023. The Australian. 9 November 2022. Retrieved 10 November 2022.
  32. Statistical Analysis and Data Mining Editorial Board
  33. About SIGKDD
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