Professor Kate Smith-Miles
Chief Investigator
Kate Smith-Miles is an ARC Australian Laureate Fellow, and Professor in the School of Mathematics and Statistics at The University of Melbourne. She was Head of School at Monash University from 2009-2014. She is currently President of the Australian Mathematical Society. She is also the inaugural Director of MAXIMA (the Monash Academy for Cross & Interdisciplinary Mathematical Applications). Her research focuses on optimisation, machine learning, time series analysis, and applications of applied mathematics to tackle interdisciplinary and industrial problems. She was awarded the Australian Mathematical Society Medal in 2010 for distinguished research, and the EO Tuck Medal from ANZIAM in 2017 for outstanding research and distinguished service to applied mathematics. She serves on the ARC College of Experts, and Chairs the Advisory Board for the AMSI Choose Maths program aiming to encourage greater participation of women and girls in mathematics.
Research Interests:
Anomaly Detection
Machine Learning
Meta-heuristics
Meta-Learning
Modelling of complex systems
Optimisation
Pattern recognition
Time Series Analysis
Projects
Publications
Book Chapters
(2016). On sampling methods for costly multi-objective black-box optimization.
Advances in Stochastic and Deterministic Global Optimization. 273–296.
Invited talks, refereed proceedings and other conference outputs
(2020). Instance Space Analysis of Combinatorial Multi-objective Optimization Problems.
2020 IEEE Congress on Evolutionary Computation (CEC). 1 - 8. doi: 10.1109/CEC48606.2020.9185664
(2019). Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites.
1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019) Workshop at the SIAM International Conference on Data Mining (SDM19), May 2‑4, 2019.
(2019). Evolving stellar models to find the origins of our galaxy.
the Genetic and Evolutionary Computation ConferenceProceedings of the Genetic and Evolutionary Computation Conference on - GECCO '19. 1129 - 1137. doi: 10.1145/3321707,10.1145/3321707.3321714
(2019). In AI we trust … or should we? Show us the stress testing!.
Australasian Joint Conference on Artificial Intelligence and Australasian Data Mining Conference.
(2019). Instance Space Analysis for Unsupervised Outlier Detection.
1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning.
(2019). Party Tricks with Numerical Linear Algebra and the Quest for Trust.
Data Science Down Under 2019.
(2018). Optimization in the Darkness of Uncertainty: when you don't know what you don't know, and what you do know isn't much!.
ANZIAM.
(2018). Does normalizing your data affect outlier detection?.
useR! 2018.
(2018). Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites.
International Joint Conference on Artificial Intelligence, Workshop of Data Science Meets Optimisation.
(2018). Outlier Detection in Non-Stationary Data Streams.
Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.
(2018). Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites.
4th Asia Pacific World Congress on Engineering.
(2018). Outlier Detection in Non-Stationary Data Streams.
2018 Joint Statistical Meetings (JSM2018).
(2018). The Liquid Handling Robot Scheduling Problem.
International Conference of Austomated Planning and Scheduling (ICAPS): Scheduling and Planning Applications woRKshop (SPARK). 18-26.
(2018). oddstream and stray: Anomaly Detection in Streaming Temporal Data with R.
useR! 2018.
(2018). Symmetry breaking in a special case of the RCPSP/max.
Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA). 315-318.
(2018). Anomaly Detection in Non-Stationary Streaming Temporal Data.
38th International Symposium on Forecasting.
(2018). About outlier detection.
AustMS 2018.
(2018). The Liquid Handling Robot Scheduling.
SPARK-18. Proceedings of 11th Workshop on Scheduling and Planning Applications Workshop, pp 18-26.
(2017). Non-parametric model of the space of continuous black-box optimization problems.
the Genetic and Evolutionary Computation Conference CompanionProceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17. 175 - 176. doi: 10.1145/3067695.3075971
(2017). Generating custom classification datasets by targeting the instance space.
the Genetic and Evolutionary Computation Conference CompanionProceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17. 1582 - 1588. doi: 10.1145/3067695.3082532
(2015). Effects of function translation and dimensionality reduction on landscape analysis.
2015 IEEE Congress on Evolutionary Computation (CEC)2015 IEEE Congress on Evolutionary Computation (CEC). 1336 - 1342. doi: 10.1109/CEC.2015.7257043
Journal Articles
(In Press). Revisiting Facial Age Estimation with New Insights from Instance Space Analysis.
IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2020.3038760
(2021). Anomaly Detection in High-Dimensional Data.
Journal of Computational and Graphical Statistics. 30(2), 360 - 374. doi: 10.1080/10618600.2020.1807997
(2021). Symmetry breaking of identical projects in the high-multiplicity RCPSP/max.
Journal of the Operational Research Society. 72(8), 1822-1843. doi: 10.1080/01605682.2019.1595192
(2021). Instance space analysis for a personnel scheduling problem.
Annals of Mathematics and Artificial Intelligence. 89, 617 - 637. doi: 10.1007/s10472-020-09695-2
(2020). A transformation technique for the clustered generalized traveling salesman problem with applications to logistics.
European Journal of Operational Research. 285(2), 444 - 457. doi: 10.1016/j.ejor.2020.01.053
(2020). Early classification of spatio-temporal events using partial information.
PLOS ONE. 15(8), e0236331. doi: 10.1371/journal.pone.0236331
(2020). Generating New Space-Filling Test Instances for Continuous Black-Box Optimization.
Evolutionary Computation. 28(3), 379-404. doi: 10.1162/evco_a_00262
(2020). Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management.
Computers & Operations Research. 119, 104941. doi: 10.1016/j.cor.2020.104941
(2020). Stochastic optimization of two-machine flow shop robotic cells with controllable inspection times: From theory toward practice.
Robotics and Computer-Integrated Manufacturing. 61, 101822. doi: 10.1016/j.rcim.2019.101822
(2020). Generation techniques for linear programming instances with controllable properties.
Mathematical Programming Computation. 12, 389-415. doi: 10.1007/s12532-019-00170-6
(2020). On normalization and algorithm selection for unsupervised outlier detection.
Data Mining and Knowledge Discovery. 12, 389-415. doi: 10.1007/s10618-019-00661-z
(2020). Parameter estimation for a point-source diffusion-decay morphogen model.
Journal of Mathematical Biology. 80(7), 2227 - 2255. doi: 10.1007/s00285-020-01494-x
(2020). Anomaly Detection in Streaming Nonstationary Temporal Data.
Journal of Computational and Graphical Statistics. 29(1), 13-27. doi: 10.1080/10618600.2019.1617160
(2019). A feature‐based procedure for detecting technical outliers in water‐quality data from in situ sensors.
Water Resources Research. 55(11), 8547-8568. doi: 10.1029/2019WR024906
(2019). Anomaly Detection in High Dimensional Data.
arXiv. arXiv:1908.04000v1.
(2019). The impact of various carbon reduction policies on green flowshop scheduling.
Applied Energy. 249, 300 - 315. doi: 10.1016/j.apenergy.2019.04.155
(2018). A cross-entropy method for optimising robotic automated storage and retrieval systems.
International Journal of Production Research. 56(19), 6450-6472. doi: 10.1080/00207543.2018.1456692
(2018). On normalization and algorithm selection for unsupervised outlier detection.
doi: 10.13140/RG.2.2.19976.26880
(2018). Instance spaces for machine learning classification.
Machine Learning. 107(1), 109 - 147. doi: 10.1007/s10994-017-5629-5
(2017). Dynamic Algorithm Selection for Pareto Optimal Set Approximation.
Journal of Global Optimization. 67, 263-282. doi: 10.1007/s10898-016-0420-x
(2017). Scheduling of two-machine robotic rework cells: In-process, post-process and in-line inspection scenarios.
Robotics and Autonomous Systems. 91, 210 - 225. doi: 10.1016/j.robot.2017.01.009
(2017). Increasing Throughput for a Class of Two-Machine Robotic Cells Served by a Multifunction Robot.
IEEE Transactions on Automation Science and Engineering. 14(2), 1150 - 1159. doi: 10.1109/TASE.2015.2504478
(2017). Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space.
Evolutionary Computation. 25(4), 529 - 554. doi: 10.1162/evco_a_00194
(2017). A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system.
Computers & Industrial Engineering. 112, 492 - 502. doi: 10.1016/j.cie.2017.02.009
(2017). Visualising forecasting algorithm performance using time series instance spaces.
International Journal of Forecasting. 33, 345-358. doi: 10.1016/j.ijforecast.2016.09.004
Publicly available softwares
Technical reports and unrefereed outputs
(2018). On normalization and algorithm selection for unsupervised outlier detection.
(2018). Anomaly Detection in Streaming Nonstationary Temporal Data.
(2018). Reliability of Exploratory Landscape Analysis.
