My current key areas of research focus on machine learning in the context of change. The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is revolutionising the field of temporal analytics. We have refocused the field on methods that are both effective and feasible for non-trivial problems. We received a prestigious best paper award at the SDM data science conference [1]; one of our papers is recognised as Clarivate Web of Science HighCite (top 1% of papers for the field of research) [2]; three of our algorithms (TS-Chief, InceptionTime and Rocket) have been independently identified as three of four algorithms that define the current state of the art [3]; our papers on these three algorithms [4-6] are the three most cited papers published in 2020 in the primary journal for time series classification research, Data Mining and Knowledge Discovery [7]; and our algorithm Rocket has been independently assessed as ‘the most important recent development in the field’ [8].

My driving passion is to create technologies that address pressing societal problems. My research is grounded in deep analysis of real-world shortcomings in state-of-the-art data science when applied to significant problems, including those providing real-world contexts for this proposal. Using critical analysis, detailed conceptual development, and careful engineering, I develop innovative solutions of wide application. The resulting effective and original algorithms and techniques are implemented in widely used impactful software.

I received the inaugural Australian Museum Eureka Prize for Excellence in Data Science in 2017. The Eureka Prizes are peak Australian research awards. I am an IEEE (Institute of Electrical and Electronics Engineers) Fellow. I received a prestigious Australian Research Council Discovery Outstanding Researcher Award, one of only 17 selected from all fields of Australian research in 2014. My contributions have also been recognised by my Australian Computer Science and Artificial Intelligence peers with the 2016 Australian Computer Society ICT Researcher of the Year Award, and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award. My publications in time-series analysis have twice won prestigious best paper awards at the SIAM Data Mining conference.

My international standing as a leading data scientist is further evidenced by my having been Editor in Chief of the premier data mining journal, Data Mining and Knowledge Discovery (2005 to 2014), and Program Committee Chair of the two top data mining conferences, ACM SIGKDD (2015) and IEEE ICDM (2010), as well as General Chair of IEEE ICDM (2012) and General and Program Chair of most leading regional and Australian AI and data science conferences. I am co-editor of the phenomenally successful Springer Encyclopedia of Machine Learning (one of Springer’s most downloaded reference works) and on several journal editorial and advisory boards.

I am the only Australian ever elected to the Executive of the peak international data mining body, the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) and am on the steering committees of five further international conference series.

I am frequently invited to present keynote addresses at international conferences and workshops.

My competitive external grants total more than $31 million, including more than $24 million from national competitive grant schemes.  

My research is published in leading data science journals and conferences.

References

1. Tan, C.W., et al., Efficient search of the best warping window for dynamic time warping, in Proceedings of the 2018 SIAM International Conference on Data Mining. 2018, SIAM. p. 225-233.

2. Pelletier, C., G.I. Webb, and F. Petitjean, Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 2019. 11.

3. Bagnall, A., et al., On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0), in International Workshop on Advanced Analytics and Learning on Temporal Data. 2020, Springer International Publishing: Cham. p. 3-18.

4. Shifaz, A., et al., TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification. Data Mining and Knowledge Discovery, 2020. 34(3): p. 742-775.

5. Dempster, A., F. Petitjean, and G.I. Webb, ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 2020. 34(5): p. 1454–1495.

6. Fawaz, H.I., et al., InceptionTime: Finding AlexNet for Time Series Classification. Data Mining and Knowledge Discovery, 2020. 34(6): p. 1963-1983.

7. Web of Science. 2021, Clarivate.

8. Middlehurst, M., et al., HIVE-COTE 2.0: a new meta ensemble for time series classification. arXiv preprint arXiv:2104.07551, 2021.