🎓 Join Us
Interested in computer vision or machine learning? I welcome enquiries from prospective master’s students, including recommendation-based admission applicants, and undergraduate students interested in research.
Our research includes video analysis and synthesis, human motion transfer, object pose estimation, salient object detection, and medical image segmentation. I hope you can find a topic that interests you and develop the skills to explore it independently.
Get in touch · yuzhu.ji@gdut.edu.cn ↗
Master’s students
If you are applying to an academic or professional master’s programme at the GDUT School of Computer Science and Technology, you are welcome to email me your CV. This includes applicants using recommendation-based admission. It would be helpful to hear about your interests and what you hope to learn during your master’s studies.
The group focuses on academic research, with an emphasis on careful experiments and strong papers. Research takes sustained effort, so please consider how this fits with your own goals. We can discuss these expectations when we meet.
Undergraduate students interested in research
I welcome first- to third-year students at the GDUT School of Computer Science who are curious about research, especially those considering further study. Previous research experience is not required to get in touch; you can tell me what you have studied, tried, or would like to explore.
Coursework remains important. I hope you can plan your time realistically and take part in research consistently alongside your studies. An initial conversation and a small research task can help us understand whether the topic and working style are a good fit.
Useful preparation
A foundation in the following areas will make it easier to get started. You do not need to have mastered everything before contacting me; areas you are still learning can be part of our initial discussion.
- Mathematics: linear algebra, probability, and statistics.
- Machine learning: introductory machine learning and deep learning, through coursework or self-study.
- Programming: an interest in understanding research code, running experiments, and working through implementation problems.
- Communication: a willingness to ask questions, share progress, and explain both your ideas and difficulties.
For a starting point, you could look at Zhi-Hua Zhou’s Machine Learning textbook, Andrew Ng’s machine learning courses, Gilbert Strang’s linear algebra courses, or Stanford’s CS231n.
Research and discussion in the group
We usually meet each week to discuss papers or research progress, with slides to help explain the work. Please bring questions as well as results: what you have tried, where you are stuck, and what you think might be worth trying next.
We work towards high-quality papers while developing skills in reading, implementation, experimental analysis, writing, and presentation. Where there are suitable research outcomes, we may also pursue invention patents or research-oriented student competitions.
How to get in touch
Please email a CV and a short introduction. The first email does not need to be long; the following information will help me get to know you. Recommendation-based admission applicants are welcome to contact me in the same way.
- A CV: your university, degree programme, relevant coursework, and English proficiency results where applicable.
- Projects or research: work you have taken part in and what you contributed. If you have not yet done research, coursework or personal projects are also useful to hear about.
- Your interests and plans: topics you would like to explore and your thoughts about further study or a research career.
To make your email easier to find, a subject such as the following would be helpful: Master’s / Undergraduate Research — Name — University.
yuzhu.ji@gdut.edu.cn ↗An initial conversation and research experience
After an initial exchange, we can arrange a conversation online or in person about your interests and plans. If there is a suitable topic, we may use a paper discussion or a small reproduction exercise to explore it further.
Read and discuss a paper
We can choose a paper together or discuss one you are interested in. A 10–15 minute presentation is a useful starting point: what problem does it address, how does the method work, what do the experiments show, and what questions do you have?
Try an experiment
You can try running an existing implementation or reproducing an experiment, then walk me through your approach and observations. Problems encountered along the way are useful discussion points too.
A preparation period of about a week is typical, with the task and timing discussed beforehand. These are research conversations to help us get to know each other; formal admissions arrangements remain subject to university and school notices.