PhD Scholarship in Federated Learning and Explainable AI at RMIT Vietnam

Scholarships are available for two local or international students to be based at RMIT Vietnam, enrolled in the RMIT Melbourne PhD (Computer Science) program (DR221). Federated learning using unlabelled sensor and user-generated data.

The scholarship includes AUD $10,000 living allowance per year and tuition fee waiver. The student will be based in Vietnam but does not need to be a Vietnam national. 

The candidate will pick one of the two projects to write their proposal on:

  1. Project title: Federated learning using unlabelled sensor and user-generated data
    As a machine learning framework mostly suited for large-scale labelled data, federated learning could be a key technology to bring fragmented sensitive data, such as live sensor monitoring data, thus enables training a shared global model to improve the accuracy of monitoring services while keeping the sensitive data where they originate. In this project, we investigate how to apply the Federated Learning model to enhance the accuracy and efficiency of the constant monitoring and diagnosis process in different domains, such as within smart cities domain, or in medical domain.
    Melbourne contact (senior supervisor): Professor Flora Salim
    VN contact (associate supervisor): Dr. Minh Ngoc Dinh
  2. Project title: Explainable AI for personalised models
    This project aims to investigate techniques to generate actionable explanations, for a range of problems and data types and modality, from large-scale unstructured data, to highly varied sensor data and multimodal data. Existing surrogate models and techniques for explaining machine learning predictions (output) or models have recently been proposed, such as LIME, but they are only limited to local explanations, based on the prediction outputs, and are not robust to the variations in sample and hyperparameter settings. Recent models such as SHAP provides global explanations, however the computation is expensive and fully factorised explanations can be fully intractable. This project seeks to advance effective and efficient surrogate models for local and global model explanations, applicable to personalised prediction and recommender systems.
    Melbourne contact (senior supervisor): Professor Flora Salim
    VN contact (associate supervisor): Dr Nhi Vo Ngoc Yen

$10,000 per year (stipend) for 3 years and full tuition-fee waiver.

To be eligible for this scholarship you must:

  • have first-class Honours or equivalent in a relevant discipline of Computer Science
  • be an Australian citizen, Australian permanent resident or an international student meeting the minimum English language requirements
  • provide evidence of good oral and written communication skills
  • demonstrate the ability to work as part of a multi-disciplinary research team
  • meet RMIT’s entry requirements for PhD (Computer Science).

To apply, please submit the following documents to Professor Flora Salim at flora.salim@rmit.edu.au:

  • a cover letter (research statement)
  • a copy of electronic academic transcripts
  • a CV that includes any publications/awards and the contact details of 2 referees.

For international applicants, evidence of English proficiency may be required.

Scholarship applications will only be successful if prospective candidates are provided with an offer for admission.

10 August 2021.

30 September 2021.

The research is aimed at generating contributions and research outputs in data mining and ubiquitous/pervasive computing field, with potential publications in top conferences such as KDD, WWW, SenSys, IMWUT, PerCom, as well as in high-impact journals.

This scholarship will be governed by RMIT University's Research Scholarship Terms and Conditions.

For further information, contact Professor Flora Salim via flora.salim@rmit.edu.au.

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Acknowledgement of country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.