For perspective students, I appreciate reading the following before reaching out to me through email.
To make it easier for me to identify the applications, use "PhD (or Postdoc, Visiting Student) Application" as your title.
When reaching out to me, it would be best to demonstrate the following in your email.
- Early reachout and collaboration with my lab can be a very effective way to stand out from all the candidates.
- Highly competitive PhD student applicants usually had abundant research experiences prior to the application.
Note that the number of publications is not the crucial factor.
- Student who had a single publication, but demonstrated outstanding ability (usually as a first author) in idea formulation,
implementation, experiments, analysis etc. is considered more competitive than a student who participated in many research works
but did not own / lead one from beginning to the end.
- Publications in top-tier ML and data mining conferences such as NeurIPS, ICML, ICLR, KDD, WebConf etc. are highly encouraged.
High-impact journal publications in interdisciplinary fields are also highly appreciated.
- It is recommended that the field of your prior research is under the broad category of machine learning.
However, the actual research topic does not need to be similar to mine,
as long as the candidate demonstrates interests and understanding of the research topics of our lab.
We welcome diversity at all levels, including skill sets!
I understand that there are students who do not yet have a conference publication yet, but are interested in applying.
I recommend highly motivated students to reach out to me way earlier than the admission deadline,
and join as a collaborator in existing projects, with the goal of a publication. I will be able to occasionally brainstorm, discuss and meet.
Major progress, achievements and paper during the project can better help me advocate for the application.
Postdoc candidates are encouraged to reach out to me as well.
- Successful candidates usually have 3 or more
solid and impactful publications
in an area, and have a coherent and unified thesis on a specific topic, encompassing a number of works.
- Similar to evaluating PhD applicants, I value paper quality over quantity.
- Prior experiences in leading a large-scope project will be appreciated.
- The candidates are required to have extensive research experiences in either foundation models, graph learning, trustworthy deep learning or relational reasoning.
After passing preliminary screening, The candidate will be asked to give a research talk (remote or in-person) to the group and talk to lab members, before
making the decision.
I welcome visiting students / internships at all levels. Students are required to demonstrate a strong interest and good background
knowledge in graph learning. Prior research experiences are encouraged but not necessary.