I obtained my PhD in 2002 from the School of Electrical Engineering and ComputerScience, University of Newcastle, in Australia. After a year as a postdoctoral research fellow at the Business and Technology Laboratory in the University of Newcastle, Australia, I joined the School of IT at James Cook University, Australia. I have been actively involved in working on broad areas of geoinformatics and intelligence informatics. My research interests include geospatial data mining, multiple classifiers, geospatial databases, conceptual spaces, Web 2.0, map segmentation, clustering, geo-visualisation, internet of things, and Voronoi tessellations. Currently, I am working for intelligence and health informatics for the Tropics.

  • CP3000: Research Topics in Technology (Level 3; TSV & CNS)
  • CP5030: Special Topics 1 (Level 5; CNS)
  • CP5035: Special Topics 2 (Level 5; CNS)
  • CP5045: Information Technology Project (Level 5; CNS)
  • CP5120: Topics in Artificial Intelligence (Level 5; CNS)
  • CP5140: Topics in Media (Level 5; CNS)
  • CP5160: Topics in Software Development (Level 5; CNS)
  • CP5170: Topics in Systems and Networks (Level 5; CNS)
  • CP5330: Special Interest Topic 1 (Level 5; CNS)
  • CP5602: Advanced Algorithm Analysis (Level 5; CNS)
  • Market analysis through online map segmentation
  • Geospatial data mining through clustering, association rules mining, and sequential mining
  • Trajectory mining for travel patterns and animal movements
  • Geocomputation, geo-engineering, geomatics and spatial data handling
  • Mobile augmented reality for learning and teaching
  • Voronoi tessellation and Delaunay triangulation applications
  • Applied artificial intelligence and machine learning algorithms
  • Data structure, computational geometry, data handling and management
  • 2010 to 2013 - Associate Professor, James Cook University (Cairns)
  • 2006 to 2009 - Senior Lecturer, James Cook University (Towsville)
  • 2003 to 2005 - Lecturer, James Cook University (Townsville)
  • 2002 to 2003 - Post-doctoral research fellow, The University of Newcastle (Newcastle)
  • 2012 - Best paper award in the 46th Hawaii International Conference on System Science
  • 2009 - Faculty Citation for Outstanding Contributions to Student Learning
  • 2007 - Best paper award in the Pacific Asia Workshop on Intelligence and Security Informatics
  • 2006 - Faculty Citation for Outstanding Contributions to Student Learning
  • 2004 - ACM (lifetime)

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles
Conference Papers

ResearchOnline@JCU stores 144+ research outputs authored by Prof Ickjai Lee from 2000 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Department of Industry - Innovations Connections

ResPax Digital Passbook (Phase II)

Indicative Funding
$98,304 over 1 year
To continue the Phase I of project to further develop digital tools for collecting tourism data in the region to enable better decisionmaking.
Ickjai Lee, Jason Holdsworth, Kurt Schoenhoff, Thomas Napier and Jarod Hine (College of Science & Engineering)
Data mining; Data integration; Artificial intelligence; Information systems

Department of Industry - Innovations Connections

Develop the ResPax (digital) Passbook

Indicative Funding
$102,429 over 1 year
To develop digital tools for collecting tourism data in the region to enable better decision-making through various data mining approaches.
Ickjai Lee, Jason Holdsworth, Kurt Schoenhoff, Thomas Napier and Jarod Hine (College of Science & Engineering)
Data Mining; Data Integration; Artificial Intelligence; Information Systems

Tropical Australian Academic Health Centre Limited - Research Seed Grants

Immersive virtual reality in a northern Queensland haemodialysis unit: A cross-over randomised controlled feasibility trial.

Indicative Funding
$18,000 over 2 years (administered by Townsville Hospital and Health Service)
This study will explore the feasibility and acceptability of an immersive VR experience for patients attending a north Queensland haemodialysis service and provide information to inform a multi-centre randomised controlled trial. Over the 4 week intervention period, participants will be offered a headset with vision of the local natural environment and with audio. Outcomes will be measured by participants: acceptability and usability of VR; attendance at scheduled dialysis sessions and adherence to lifestyle modifications; wellbeing, anxiety and depression; adverse events such as nausea. The feasibility and acceptability of the equipment from the clinicians? perspectives will also be explored.
Wendy Smyth, Cate Nagle, Joleen McArdle, John Body-Dempsey, Valli Manickam, Anne Swinbourne, Ickjai Lee and Jason Holdsworth (Townsville Hospital and Health Service, College of Healthcare Sciences and College of Science & Engineering)
Virtual reality; Haemodialysis; Distraction Therapy

The World Wide Fund for Nature, Australia - Contract Research

JCU Spawning Potential app development

Indicative Funding
$37,500 (administered by World Wide Fund for Nature Australia)
The central biological measure of success for the ?Community- Based Sustainable Development in Solomon Island and PNG Coastal Communities? projects are trends in the Spawning Potential Ratio (SPR) of key target species. This Project seeks to refine the Spawning Potential Survey (SPS) App (JCU FISH) to include spatial reporting tools that can be utilised by survey participants to monitor spatial and temporal trends in SPR. This project extends from an earlier ?proof of concept? project funded by the WWF Ocean Practice where the potential for automatic identification and measurement of target species from a single image was realised.
Marcus Sheaves, Ickjai Lee, Jason Holdsworth and Michael Bradley (College of Science & Engineering)
Reef Fish; Pacific Islands; Fisheries; App; Catch data; Monitoring

Department of Industry - Innovations Connections

Develop an autonomous AI vehicle damage assessment tool (Phase II)

Indicative Funding
$49,894 over 1 year, in partnership with Hello Claims Pty Ltd ($49,895)
Improvement upon the project 1 developed method for automated detection, identification and categorisation of vehicle panel damage by developing a user interface and video vision. Semantic segmentation and deep learning networks will be further refined and developed for the next evolution/iteration of the software platform.
Ickjai Lee, Komal Khan, Kurt Schoenhoff and Thomas Napier (College of Science & Engineering)
Image classification; Deep learning; Data recognition; Semantic segmentation

Fisheries Research & Development Corporation - Annual Competitive Round

Application of a machine learning approach for effective stock management of abalone

Indicative Funding
$115,649 over 2 years
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R&D improvements. Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems, and render the product data to be accessible and applicable for farmers.
Jan Strugnell, Marcus Sheaves, Carlo Mattone, Ickjai Lee, Joanne Lee, Jason Holdsworth and Art (Hemmaphan) Suwanwiwat (College of Science & Engineering)
Abalone (Haliotidae); Machine Learning

World Wide Fund for Nature (US) - Contract Research

From Coastal Communities to Cloud Communities ? New Application and Artificial Intelligence to Monitor Fish Stocks Using Photos ? Application Development

Indicative Funding
$53,100 over 1 year
The Project aims at develop an artificial intelligence capable to autonomously identify fish species and number from images collected at fish markets in remote location, so that effective catch rate can be evaluated and management policies can be developed.
Marcus Sheaves, Carlo Mattone, Michael Bradley, Joanne Lee, Jason Holdsworth, Art (Hemmaphan) Suwanwiwat and Ickjai Lee (College of Science & Engineering)
Artificial Intelligence; Phone App; Caught Fish; Catch Rate

Department of Industry - Innovation Connections - Entrepreneurs' Programme

Develop an autonomous AI vehicle damage assessment tool

Indicative Funding
$50,000 over 1 year, in partnership with Hello Claims Pty Ltd ($50,211)
Development of a practical means and method for automated detection, identification and categorisation of vehicle panel damage using AI, deep learning and selected semantic segmentation networks. Semi-Supervised methods will be investigated to minimize the requirements on hand made training data (archived damaged vehicle images) and research will be conducted to determine the possibility to build a minimum viable product (MVP) directly onto the existing platform architecture.
Ickjai Lee and Aidan Possemiers in collaboration with Kurt Schoenhoff (College of Science & Engineering)
Data recognition; Image Classification; Semantic segmentation; Deep Learning

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

  • Deep Learning Augmented Anomaly Detection for Flight Data (PhD , Secondary Advisor/AM)
  • Correlation Clustering Algorithm based on Differential Privacy (PhD , Secondary Advisor/AM)
  • Unravelling the misuse of large language models for phishing attacks (Masters , Secondary Advisor)
  • Sustaining Chinese Mazu Cultural Heritage in Tourism through VR Technology: Insights from East and Southeast Asia (PhD , Advisor Mentor)
  • A Study of Human Resource Training and Front-Line Service Quality through Gamification in the Hospitality Industry: Comparisons from the Hotel Sector in Singapore, Kuala Lumpur and Colombo (PhD , Advisor Mentor)
  • Efficient Semantic Segmentation using Deep Learning (PhD , Primary Advisor/AM/Adv)
  • Autonomous CNN architecture selection and dynamic modification through proven convolutional architectures and transfer learning (PhD , Primary Advisor/AM/Adv)
  • Species Classification using deep learning-Based signal processing techniques in natural soundscapes (PhD , Primary Advisor/AM/Adv)
  • Biokinetics modelling and optimization on the basis of Differential Privacy (PhD , Secondary Advisor/AM)
  • Enhancing Explainability in Large Language Models through Attention Visualization and Rule-Based Explanation (PhD , Secondary Advisor/AM)
  • HCI mobile eduation: Integrating learning, fun and turning addiction into performance (PhD , Secondary Advisor/AM)
  • Representing user behaviour profile in space for robust, non-invasive, adaptive, high performing, efficient, and continuous user authentication (PhD , Secondary Advisor/AM)
  • Lightweight Self-supervised learning for Image Classification and Object Recognition (PhD , Secondary Advisor/AM)
  • User behaviour Anomaly Detection through Spatio-temporal Trajectories (PhD , Primary Advisor/AM/Adv)
  • Contact mining from spatio-temporal trajectories (PhD , Primary Advisor/AM/Adv)

These are the most recent metadata records associated with this researcher. To see a detailed description of all dataset records, visit Research Data Australia.


The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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