Information Technology
Umair Qureshi
- Senior Lecturer, Information Technology
- umair.qureshi@jcu.edu.au
Chao Chen
- Adjunct Senior Lecturer
- chao.chen@jcu.edu.au
Dmitry Konovalov
- Senior Lecturer
- dmitry.konovalov@jcu.edu.au
Trina Myers
- Adjunct Professor
- trina.myers@my.jcu.edu.au
Iti Chaturvedi
- Lecturer, Information Technology
- iti.chaturvedi@jcu.edu.au
Euijoon Ahn
- Senior Lecturer, Information Technology
- euijoon.ahn@jcu.edu.au
Ickjai Lee
- Professor - Promotional Chair
- ickjai.lee@jcu.edu.au
Kyungmi Joanne Lee
- Senior Lecturer
- joanne.lee@jcu.edu.au
Kranthi Addanki
- Lecturer, Information Technology
- kranthi.addanki@jcu.edu.au
Lindsay Ward
- Senior Lecturer
- lindsay.ward@jcu.edu.au
Augmented Reality to develop primary school students' balancing skills
Partnership with GymbaROO in Victoria to develop and evaluate augmented reality tools and software for developing balancing skills in primary school children.
City of Casey Citizen Engagement Chatbot and Social Isolation Project
Collaboration with City of Casey Council and interdisciplinary colleagues from Nursing to develop GIS tools for evaluating social isolation epidemic in CALD populations of Casey Cardinia regions of Victoria.
Refining the bidirectional encoder representations from transformer (BERT) model for semantic intent classification using Twitter data
Improve classification accuracy on downstream tasks of BERT model in noisy and low-data regimes like Twitter.
Passive monitoring and intervention for fathers' experiencing perinatal distress
Training and evaluating prediction models on social media for fathers' perinatal depression and anxiety.
Harnessing social media and m-health to predict, monitor and intelligently intervene in paternal postpartum depression
Developing and evaluating predictive models for parents' depression and anxiety on social media, informing the development of chatbots and personalised mobile interventions.
Sentiment prediction from social media
This project looks at large scale product recommendation from social media such as YouTube videos and Twitter. We are also developing unity based games for data collection and validation of our algorithms.
Immersive virtual reality in a northern Queensland haemodialysis unit: A cross-over randomised controlled feasibility trial. (Old ID 27236)
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.
Machine learning approach to restoration, prediction and quality control of oceanographic data from IMOS Moorings (Old ID 24926)
This project investigates a machine learning approach to increasing the value of oceanographic data. The full collection of IMOS Moorings data will be available for use in developing and training algorithms. Much of this data has already been flagged by heuristic quality control routines, and manually annotated by domain experts.
JCU Spawning Potential app development (Old ID 27727)
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.
Urban crime analysis through areal categorized multivariate association mining
- 2008
- Taylor & Francis
- Researchers:Ickjai Lee
Modified Simpson O(n3) algorithm for the full sibship reconstruction problem
- 2005
- Oxford University Press
- Researchers:Dmitry Konovalov
Start Date:
01 Jan 2002
End Date:
01 Jan 2003
Start Date:
01 Jan 2007
End Date:
01 Jan 2008
Start Date:
01 Jan 2024
End Date:
01 Jan 2026
Start Date:
01 Jan 2020
End Date:
01 Jan 2021
Start Date:
01 Jan 2020
Start Date:
01 Jan 2025
Start Date:
01 Jan 2020
Title:
JCU Open day
Title:
JCU Inspiration on Tap
Start Date:
01 Jan 2023
Start Date:
01 Jan 2017