Scalable and Lightweight On-Device Recommender Systems
- Enrolment status
- Future UQ student
- Student type
- Domestic, International
- Study level
- Postgraduate research (HDR)
- Study area
- Computer science and IT
- Scholarship focus
- Academic excellence
- Funding type
- Living stipend, Tuition fees
- Scholarship value
- $29,863 per annum (2023 rate), indexed annually
- Scholarship duration
- 3.5 years with the possibility of 1 extension in line with UQ and RTP Scholarship Policy
- Number awarded
- May vary
- Applications open
- 30 September 2022
- Applications close
- 31 March 2023
About this scholarship
Supervisor: Dr Tong (Rocky) Chen
This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage. The ideal candidates are expected to have the following skills:
Research environment
The research is based in the Data Science Discipline at School of ITEE, The University of Queensland. The Data Science discipline conducts world-leading research and develops innovative and practical solutions for business, scientific and social applications in the realm of big data. The Data Science Discipline has a well-established portfolio of web-scale user datasets and data-intensive computing infrastructure available to the applicant, including SAP’s High-performance Analytic Appliance (HANA) machine with 1TB RAM, FlashLite with 70 nodes and 2400 cores, and over 20 latest GPU clusters, which can support terabyte-level high-performance storage, processing and model training.
Eligibility
You're eligible if you meet the entry requirements for a higher degree by research.
How to apply
Before submitting an application you should:
- check your eligibility for a Doctor of Philosophy (PhD)
- prepare your documentation
- contact Dr Tong (Rocky) Chen (tong.chen@uq.edu.au) to discuss your suitability for this scholarship
You apply for this scholarship when you submit an application for a Doctor of Philosophy (PhD). You don't need to submit a separate scholarship application.
When you apply, please ensure that under the scholarships and collaborative study section you:
- Select ‘My higher degree is not collaborative’
- Select 'I am applying for, or have been awarded a scholarship or sponsorship'.
- Select ‘Other’, then ‘Research Project Scholarship’ and type in ‘SCALABLE-CHEN’ in the 'Name of scholarship' field.
Selection criteria
Your application will be assessed on a competitive basis.
We take into account your:
- previous academic record
- publication record
- honours and awards
- employment history
A working knowledge of deriving state-of-the-art machine learning approaches for real-world applications and publishing conference/journal papers on prestigious venues would be of benefit to someone working on this project.
You will demonstrate academic achievement in the field/s of machine learning, predictive analytics, recommender systems, and edge intelligence and the potential for scholastic success.
A background or knowledge of model compression, decentralized machine learning, and information retrieval is highly desirable.
Rules
A domestic part-time student with carer’s responsibilities, a medical condition or a disability, which prevents them from studying full time may be eligible for scholarship consideration, on a case-by-case basis.