Available PhD Scholarships

Currently, I have some full PhD scholarships for prospective students in 2023 and 2024. A full PhD scholarship will cover 100% tuition fee and provide the student with an annual stipend of 33500 AUD tax exempt (2023 rate). More details can be found here and here. Students interested in doing a PhD in Machine Learning and Artificial Intelligence (ML&AI) under my supervision please send me applications via email.

Research Topics

Below, I list some of my broad research topics which I expect prospective students will work on under my supervision. If you are interested in these topics or have any other research proposals, don't hesitate to contact me and we can discuss further 😄.

  • Generative Models
  • Large Vision-Language Models
  • Representation Learning
  • Robust and Generalizable Artificial Intelligence
  • Label and Data Efficient Deep Learning

Required Documents

When you contact me, please send me the following documents via email:

  • A comprehensive resume (CV)
  • Theses
  • Academic transcripts
  • Published papers and scientific reports in English (if any)
  • Recommendation letters from your previous teachers/supervisors/seniors (if any)
  • A globally accepted English language certificate (e.g., IELTS, TOEFL, PTE)
  • A research proposal specifying at least three reseach problems you aim to address during your PhD and your general solutions to these problems

Please note that the above documents must be written in or translated to English. My list of documents may not be exhaustive and you are encouraged to include other documents you think could demonstrate your research capability.

Selection Criteria

  • Research inclination: Students should be intrigued by understanding why and how things work in a deep and generalizable way, and creating novel and better methods to solve their problems rather than just reimplementing existing models naively.
  • Research experience: Publishing high-quality papers in top-tier ML&AI conferences and journals requires many advanced research skills such as choosing problems, forming and validating ideas, conducting experiments, writing papers, ... which take lots of time to master. Therefore, students with relevant reserach experience are preferred to those without. In addition, students should have their mind well-prepared for possible difficulties of doing PhD since in reality, things could be very different from what they have thought.
  • Academic achievements: Academic achievements are recognitions of students' efforts and abilities to attain some challenging targets during previous studies, which, to a certain extent, could suggest how well the students will perform when doing PhD. Therefore, students with significant academic achievements tend to be more favored than others.
  • Good mathematical background: Math is important for high-quality research in ML&AI as it stands behind most advances in the field. Some branches of math commonly used in ML&AI research include statistics, optimization, algebra, and calculus. It will be advantageous if students had high GPAs for math courses and can demonstrate their deep knowledge about these branches of math during interview.
  • Good programming skills: While deep mathematical knowledge enables students to design correct methods, good programming skills enable students to implement their methods effectively and efficiently, hence, will increase their productivity. One important programming skill is the ability to code in an organized and reusable way so that the students and possibly other people could inherit and develop new methods from their old code bases. For this criterion, students who experienced working as a main programmer in academic or industrial projects will be valued more.
  • Good English: The minimum level for English language proficiency is the Academic IELTS overall score of at least 6.5 with no individual component scores less than 6.0. For more details about this requirement and equivalent scores for other kinds of English test such as PTE Academic or TOEFL iBT, please refer to the table in Part C, Section 5 from this link. Please note that a student could be interviewed without meeting this requirement, but this requirement is compulsory for the student to be granted a PhD scholarship.