Prospective Students

Jul 30, 2021 • Zhiming Zhou

I’m looking for talented students with enthusiasm for fundamental research in machine learning / artificial intelligence.

• Overall:
  • I have a very high standard for research and require students to do RIGOROUS and IN-DEPTH research INDEPENDENTLY.

• Our target:
  • Top research that (at least) can be published in top conferences/journals, like ICML/NIPS/JMLR.
  • I personally prefer fundamental and theoretical research that has practical impacts.

• The first thing you will need to do when you become one of my students:
  • Read 20 ~ 50 papers from top conferences/journals.
    • In any topic: the one(s) you are interested in, or just any.
  • Make sure to understand the following key information in each paper:
    • What is the problem they are solving?
    • What is their main contribution?
    • What is their main difference compared with previous work?
  • What is your judgment? What do you think of this paper?
    • Is the problem important?
    • Is their contribution significant?
    • Are their theoretical/experimental results solid?
  • Do you have any criticism of them?
    • Try to criticize their motivation, their assumption, their logic, their solution/theory, their experiment, etc.
    • Is there any limitation? Did they overlook any important issues/aspects? Can you do better?
  • Note that this typically requires a good high-level understanding, while details are often not necessary and can be omitted.

• What will be your research topic:
  • We DO NOT assign topics to students in principle.
  • Find your research topic on your own by reading papers and I will always be there if you need any guidance.
  • When you have read 50 papers and still haven’t found a suitable/interested topic, I will give my suggestions.

• My style:
  • I might be generally very nice, but I can be rather critical upon research.
  • You will be required to build a DEEP and CLEAR understanding of your research topic and related works.
    • This may take a long time, but it will be a PREREQUISITE for us to start the real research.
  • I will guide and force you to think deeply, clearly, and logically. Wish we have the same belief that:
    • Superficial understanding may lead to specious conclusions.
    • Deeper thinking can bring simpler/better solutions.

• Characteristics that I value most:
  • [5’] Good (SELF-)LEARNING ability.
  • [5’] CLEAR and STRICT LOGIC.
  • [4’] Good intuition and critical thinking.
  • [3’] Good at math and programming.
  • [3’] Confidence and perseverance.

• Management:
  • There will be a weekly meeting, which is typically online and one-on-one.
  • You will be required to keep a diary of research, which records daily summaries of:
    • New things you learn;
    • New papers and/or related content you read;
    • New thoughts about your research topic or the related;
    • New experiments and/or their analysis, and so on.
  • You will be required to summarize and review your research monthly in writing.
  • There will be a PROBATIONAEY period of one semester for every newcomer.

• Principle of authorship:
  • We determine the authorship according to the contribution. The tentative score-based rules are as follows:
    • Idea: 0 ~ 2 points, depending on the importance.
    • Theory: 1 ~ 4 points, depending on the importance and workload.
    • Coding: 0 ~ 3 points, depending on the workload.
    • Writing: 1 ~ 2 points, depending on the workload.
  • The points of each part will be allocated to persons who participated, according to their contribution.
  • For conferences/journals that typically do not regard authors as of equal contribution:
    • The authors with higher contribution points have a higher priority in choosing the position in the author list.
    • For authors that share similar contributions, the relatively lower-ranked can argue for a claim of equal contribution.
      • E.g., their difference in contribution points is less than 1.
  • In principle, people with too little contribution will not be regarded as one of the authors.
    • E.g., get less than 1 point, even though somehow participated in.