2026 Kate Smith-Miles: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	49	5785	5619	176	175	209	13040

Overview

Kate Smith-Miles is affiliated with the University of Melbourne in Australia. Their research primarily spans the fields of Computer Science and Engineering, with significant contributions in subfields such as Artificial Intelligence, Industrial and Manufacturing Engineering, Computational Theory and Mathematics, Civil and Structural Engineering, and Management Science and Operations Research.

The scientist's work covers a range of topics including:

Machine Learning and Data Classification
Water Systems and Optimization
Advanced Multi-Objective Optimization Algorithms
Anomaly Detection Techniques and Applications
Data Stream Mining Techniques
Urban Stormwater Management Solutions
Machine Learning and Algorithms

Kate Smith-Miles has published multiple papers in diverse scholarly venues, with notable frequent venues being:

arXiv (Cornell University)
Computers & Operations Research
INFORMS journal on computing
European Journal of Operational Research
Journal of Water Resources Planning and Management

Recent publications reflect a focus on optimization, machine learning applications, and water resources management. Selected recent papers include:

A transformation technique for the clustered generalized traveling salesman problem with applications to logistics (2020), European Journal of Operational Research
Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management (2020), Computers & Operations Research
Leakage Detection in Water Distribution Systems Based on Time-Frequency Convolutional Neural Network (2020), Journal of Water Resources Planning and Management
Burst Detection in District Metering Areas Using Deep Learning Method (2020), Journal of Water Resources Planning and Management
China's enhanced urban wastewater treatment increases greenhouse gas emissions and regional inequality (2022), Water Research

The scientist has collaborated frequently with several coauthors, including:

Mario Andrés Muñoz
Shuming Liu
Ana Carolina Lorena
Tim D. Fletcher
Nicolau Andrés-Thió

Kate Smith-Miles's body of work demonstrates interdisciplinary engagement across computer science and engineering domains, particularly focusing on the development and application of machine learning techniques to optimization problems and environmental systems.

Best Publications

Automatic Age Estimation Based on Facial Aging Patterns

Xin Geng;Zhi-Hua Zhou;K. Smith-Miles

1247
Citations
Characteristic-Based Clustering for Time Series Data

Xiaozhe Wang;Kate Smith;Rob Hyndman

862
Citations
Cross-disciplinary perspectives on meta-learning for algorithm selection

Kate A. Smith-Miles

702
Citations
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research

Kate A. Smith

541
Citations
On learning algorithm selection for classification

Shawkat Ali;Kate A. Smith

487
Citations
Neural networks in business: techniques and applications for the operations researcher

Kate A. Smith;Jatinder N.D. Gupta

415
Citations
On chaotic simulated annealing

L. Wang;K. Smith

266
Citations
Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series

Xiaozhe Wang;Kate Smith-Miles;Rob Hyndman

266
Citations
Measuring instance difficulty for combinatorial optimization problems

Kate Smith-Miles;Leo Lopes

254
Citations
Static and dynamic channel assignment using neural networks

K. Smith;M. Palaniswami

227
Citations
Web page clustering using a self-organizing map of user navigation patterns

Kate A. Smith;Alan Ng

223
Citations
Towards objective measures of algorithm performance across instance space

Kate Smith-Miles;Davaatseren Baatar;Brendan Wreford;Rhyd Lewis

212
Citations
Visualising forecasting algorithm performance using time series instance spaces

Yanfei Kang;Rob J. Hyndman;Kate Smith-Miles

204
Citations
An Analysis of Customer Retention and Insurance Claim Patterns using Data Mining: A Case Study

Kate A Smith;Robert J Willis;Malcolm Brooks

203
Citations
A meta-learning approach to automatic kernel selection for support vector machines

Shawkat Ali;Kate Amanda Smith-Miles

183
Citations
Neural techniques for combinatorial optimization with applications

K. Smith;M. Palaniswami;M. Krishnamoorthy

172
Citations
ODAM: An optimized distributed association rule mining algorithm

M.Z. Ashrafi;D. Taniar;K. Smith

164
Citations
Instance spaces for machine learning classification

Mario A. Muñoz;Laura Villanova;Davaatseren Baatar;Kate Smith-Miles

163
Citations
Parallel Fuzzy c-Means Clustering for Large Data Sets

Terence Kwok;Kate A. Smith;Sebastián Lozano;David Taniar

150
Citations
Towards insightful algorithm selection for optimisation using meta-learning concepts

K.A. Smith-Miles

138
Citations
Facial age estimation by learning from label distributions

Xin Geng;Kate Smith-Miles;Zhi-Hua Zhou

87
Citations

Frequent Co-Authors

Xin Geng Southeast University

Zhi-Hua Zhou Nanjing University

Liang Wang Chinese Academy of Sciences

Sui Huang University of Calgary

Wee Keong Ng Nanyang Technological University

Richard Weber University of Chile

Chung-Hsing Yeh Monash University

Paolo Falcaro Graz University of Technology

Alexander Heger Monash University

Anita J. Hill Commonwealth Scientific and Industrial Research Organisation

External Links

Personal website of Kate Smith-Miles

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