About

 

 

Dr. Joanne Lee received her PhD degree in Computer Science in 2007 from Griffith University, Australia. She started her academic career as a lecturer in School of Business and IT at Charles Sturt University, Australia (2007-2008) and continued her academic pursuit after moving to James Cook University.

Joanne is an active researcher, and her research interests include machine learning, algorithm optimisation, neural networks, data mining, and applied artificial intelligence. She has been involved in various projects developing a real-world scheduling optimisation system for fly-in-fly-out mining employee scheduling, designing and implementing a non-destructive neural network based classification system for ultrasonic signals, and developing spatio-temporal mining algorithms for moving objects.

Joanne’s teaching experience covers from introductory machine learning, database, data mining, 3D modelling, and programming. Currently, she is supervising 5 PhD students, and 3 of them are near completion.

Teaching
  • CP1404: Programming II (Level 1; CNS)
  • CP2403: Information Processing and Visualisation (Level 2; CNS & TSV)
  • CP2404: Database Modelling (Level 2; CNS & TSV)
  • CP2405: Collective Intelligence and Entrepreneurship (Level 2; CNS & TSV)
  • CP3413: Information Processing and Visualisation (Level 3; CNS & TSV)
  • CP5635: Collective Intelligence and Entrepreneurship (Level 5; TSV)
Experience
  • 2007 to 2008 - Lecturer, Charles Stuart University (Albury-Wodonga)
Research Disciplines
Socio-Economic Objectives
Publications

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
More

ResearchOnline@JCU stores 54+ research outputs authored by Dr Joanne Lee from 2003 onwards.

Current Funding

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

Fisheries Research & Development Corporation - Annual Competitive Round

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

Indicative Funding
$115,649 over 3 years
Summary
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.
Investigators
Jan Strugnell, Marcus Sheaves, Carlo Mattone, Ickjai Lee, Joanne Lee, Jason Holdsworth and Art (Hemmaphan) Suwanwiwat (College of Science & Engineering)
Keywords
Abalone (Haliotidae); Machine Learning

World Wide Fund for Nature - 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 2 years
Summary
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.
Investigators
Marcus Sheaves, Carlo Mattone, Michael Bradley, Joanne Lee, Jason Holdsworth, Art (Hemmaphan) Suwanwiwat and Ickjai Lee (College of Science & Engineering)
Keywords
Artificial Intelligence; Phone App; Caught Fish; Catch Rate
Supervision

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.

Current
  • An Autonomous Tool to Produce Prototype Neural Networks for Deep Learning (PhD , Secondary Advisor)
  • Efficient Semantic Segmentation using Deep Learning. (PhD , Secondary Advisor)
  • Fast Remote Diagnosis of Bowel Activities (PhD , Secondary Advisor)
  • Predictive Spatio-Temporal Modelling With Neural Networks (PhD , Secondary Advisor)
  • Adaptive traffic surveillance system using machine learning for monitoring unlawful conducts on the road (PhD , Secondary Advisor)
  • Establishing an Empirical basis for the Curriculum and Assessment of the Competency based Fellowship of the Royal Australian and New Zealand College of Psychiatrists by Text Mining the Psychiatric Literature (PhD , Secondary Advisor)
Completed

Connect with me
Share my profile
Share my profile:
jcu.me/joanne.lee

Email
Phone
Location
Advisory Accreditation
Primary Advisor
Find me on…
Icon for Scopus Author page Icon for Google Scholar profile Icon for ORCID profile Icon for external homepage Icon for ResearchGate profile Icon for Academia.edu profile

Similar to me

  1. Dr Iti Chaturvedi
    College of Science & Engineering
  2. Dr Mangalam Sankupellay
    College of Science & Engineering
  3. A/Prof Trina Myers
    College of Science & Engineering
  4. Prof Ickjai Lee
    College of Science & Engineering
  5. Dr Oyelola Adegboye
    Australian Institute of Tropical Health & Medicine