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
- 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.
- 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.
- To develop methodologies of autonomous reinforcement learning (ARL) for sequential decision making in massive agent-environments to support decisions under sequential interactions.
Long description table of previous image
| Complex, dynamic situations with uncertainty | Autonomous learning program | Decision making (dm) |
|---|---|---|
| Massive domains | 1. Autonomous transfer learning | Cross-domain DM |
| Massive streams | 2. Autonomous drift learning | Real-time DM |
| Massive agent environments | 3. Autonomous reinforcement learning | Sequential DM |
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.
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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).
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