Our CIRES family is growing!
Our first cohort of PhD students has commenced at the ARC Training Centre for Information Resilience (CIRES). Welcome Krishna Dermawan, Samual MacDonald, Daisy Xu and Hechuan Wen pictured here at CIRES HQ at The University of Queensland.
Krishna Dermawan will be working on the Improving Sepsis Management through Better Data and Rapid Learning project in collaboration with our government partner Queensland Health, and supervised by Professor Marta Indulska and Dr Ida Asadi-Someh.
Samual MacDonald will be working on the Advancing Deep Neural Network Reliability During Dataset Shift project in collaboration with our industry partner Max Kelsen, and supervised by Dr Fred Roosta-Khorasani.
Hechuan Wen will be working on the Using Data to Overcome Wellbeing Challenges Across the Life Spectrum project in collaboration with our government partner Health & Wellbeing Queensland, and supervised by Dr Tong Chen and A/Prof Hongzhi Yin.
Daisy Xu will be working on the Defining and Measuring Analytics Value project in collaboration with our industry partner Aginic, and supervised by Professor Marta Indulska and Dr Ida Asadi-Someh.
Welcome also to Lufan Zhang who has commenced at Swinburne University of Technology working with Paul Scifleet, Amir Aryani and the team at Astral.



Congratulations to University of Queensland Researcher and Centre Chief Investigator Associate Professor Hongzhi Yin who has been awarded a prestigious Australian Research Council (ARC) Future Fellowship for the project “Decentralised Collaborative Predictive Analytics on Personal Smart Devices”. The 2021 ARC Future Fellowships scheme will see 100 new research projects funded at universities around Australia, focused on areas of national priority. Future Fellowships provide successful researchers with an opportunity to dedicate four years to their research endeavours in Australia. Hongzhi’s project will tackle the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data.