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Explore the intended outcome of the Dist. Prof. Jie Lu AO's Laureate project: Autonomous Learning for Decision Making in Complex Situations.

3 Technical Programs of Autonomous Learning for Decision Making:

Dist. Prof. Jie Lu AO's ARC Laureate Project 
  1. To develop methodologies of autonomous transfer learning (ATL) for cross-domain decision support systems and recommender systems in massive domains with uncertain, data insufficient and dynamic environments.
  2. To develop methodologies of autonomous drift learning (ADL) for real-time prediction, recommendation, and decision-making in massive data streams to support decisions given unpredictable stream pattern changes.
  3. To develop methodologies of autonomous reinforcement learning (ARL) for sequential decision making in massive agent-environments to support decisions under sequential interactions.
Three technical programs of the project - Transfer Learning, Drift Learning and Reinforcement Learning

 

Applications, translation and impact

The intended outcomes of the project include original ATL/ADL/ARL machine learning methodologies associated with algorithms, demonstrated prototypes and applications, which will have a transformational impact in most industries in Australia because machine learning is changing business prediction and decision-making processes. The outcomes will significantly improve the timeliness and quality of decision-making driven by data and will directly contribute to Australia’s capacity for artificial intelligence. 

Selected applications areas and partnering with industry

  • Healthcare - such as Workforce Health Assessors and 23Strands to improve assessment, analysis and prediction in healthcare.
  • Transportation - such as Sydney Trains to implement auto rail replacement bus planning and optimise transportation.
  • Agriculture and Logistics - such as Blu Logistics to enhance supply chain management.

National and international collaboration

We have established research collaborations with research communities such as IEEE computational intelligence society (CIS), FLINS/ISKE society; with other universities and research centres such as with University of Jaén, SusTech, Shanghai University, and many world-leading researchers. 

AI hub provides a repository for AI-related content and results shared among researchers in the world and also shows our connections. 

Collaborate with us

jing.zhao@uts.edu.au


Drone collaborations

junyu.xuan@uts.edu.au

Get involved

Collaborate with us in creating cutting-edge research for machine learning on prediction and decision-making. 

We are excited to work with Australian and international industry partners who have any requirements in machine learning, data analytics, prediction, personalised services, data-driven decision making, recommender systems and drone applications.

We also have excellent opportunities for PhD candidates, postdocs and academic visitors who have a solid research track record that includes publications in prominent sources (e.g. NeurIPS, ICML, AAAI, ICLR and other top conferences or JAI, TFS, TNNML, etc. top journals).

 EXPLORE: ARC Laureate Keynote Speeches