Below are answers to some frequent questions in The Definitive 'what do I ask/look for’ in a PhD Advisor Guide. Hopefully, this will give you ideas of what does it look like to work with me.
What is the lab structure? (how collaborative/disjointed are lab members’ projects?)
A: Each student in the lab has projects to lead. Lab members use slack channels to communicate and are freely to collaborate with each other if there is a match.
Does the advisor consider themselves a ‘hands-on’ or ‘hands-off’ advisor?
A: Both. For junior students, I tend to be more hands-on and help iron out many low-level details to the best extent I can. As students become more senior/mature, I would gradually transit to a hands-off advisor.
How does the advisor give feedback on papers/what is their feedback style?
A: I give specific and detailed feedbacks on every single figure and table. Whenever time permits (i.e., no deadline pressure), I would provide feedbacks on how the students can revise the paper. The students revise the paper. Then I give another round of suggestions. It's a slow iterative process, but it's an excellent way of training writing skills. With deadline pressure, I would revise the paper directly. Using overleaf the students can learn from the before/after comparison.
How often does the advisor meet with their students? (1:1 or all together? Daily guidance by PI?)
A: Aside from project meetings, we have weekly 30-mins 1:1 for each student. I also set aside late-night discussions time slots (often 9:00 PM - 11:00 PM) where students can sign up if they want to have more in-depth discussions.
Are there lab meetings? What are other meetings you will see your advisor in a group with other people?
A: 1) Group meetings: Everyone talks about what they are doing in the past week and what they will do next week. 2) Reading group: Presenting/discussing interesting papers in the field.
What does a group/lab meeting look like? (Or other relevant meetings)
A: It's like a standup meeting. Each person talks about their work and plan for 2-4 mins.
How many students are in the group? (Number of undergrad/masters/phd/post doc)
A: Check out our homepage.
What progress does the advisor generally expect from a student in the course of a semester? (Submission/Publication pace)
A: I expect the student complete and submit a project to one of the top conferences every two semesters. It does not have to be accepted because the reviewing could be noisy, but it's important to complete a project and submit it.
“What expectations does the advisor have for PhD graduation.”
A: I expect the student complete 2 first-authored papers before the quals exam and have 3 more papers accepted at ML conferences, e.g., NuerIPS/ICML/ICLR before the graduation. I also expect the student complete at least once research internship and have teaching assistant and guest lecture experiences. Before graduation, proposal writing experience is required and one of research projects can be converted into a proposal. The objective is to make sure the student can independently identify research gaps, find research direction, formulate research problems, solve the research problems, and present the research to the community.
“When have you given a letter of concern? Why?”
A: When a student does not meet the expectation (completing and submitting a work in two semesters including summer semester) and is not on track.
“What do you do when students are struggling”?
A: First try to identify the problems, e.g., motivation, external obligations, implementation skills, communication issues? Then, discuss with the student to come up with strategies for addressing the problems.
What PhD agenda does the advisor receommend?
A: I would recommend a PhD agenda with 4 phases as follows. Phase 1 for semester 1, 2, 3 (Practice and Preparation Period). The objective is to learn basic research skills and receive rigorous academic training.
How directly applicable will your future technical skills be to the roles you want after graduating. (If set on industry)
A: I would imagine that most of the future technical skills on improving the efficiency, privacy and security of machine learning and deep learning will be applicable to many (either research or engineering) roles in industry.
What ‘research methods’ does the lab use? (What ‘types’ of papers / contributions / conferences targeted)
A: Our work is often empirically driven and focuses on developing robust algorithms that improve the efficiency, privacy and security of machine learning and deep learning systems. Target conferences are Learning NeurIPS/ICML/ICLR; Vision: CVPR/ICCV/ECCV; For work that we wish to extend, we also publish at Security: CCS/IEEE S&P/ USENIX Security; Architecture: ASPLOS/ISCA/DAC.
“What are some of the projects that you and your students are currently working on”?
A: We are working on various machine learning projects, including private/efficient deep learning, Secure deep learning for applications in computer vision, natural language processing, and science.
“In general, do you tend to give your students projects or have them select their own”?
A: It depends. I prefer students to develop and pursue their own ideas. However, this could be challenging for junior students. For junior students, I tend to constrain the project scope a bit so that they can focus on developing their research skills.
“Do you have particular projects that you see me working on”?
A: It depends on your background and interests.
“How much freedom do you think I’d have in selecting my own projects”?
A: We need to brainstorm together to find projects (or research directions) that we both are passionate about. For example, if you select a project that is entirely outside my expertise, then I won't be able to provide the support you need.
“Are there other students you are interested in working with? If so, what would they be working on project-wise”?
A: I will be able to answer this after reviewing the pool of applicants.
“Would they have their own line of work or contribute to a bigger project/someone else’s project”?
A: It's up to you. My experience is that leading a project yourself is the best way to get you started for learning how to do research.
If your advisor made you work on a project in their area that you are least interested in (e.g. for a grant) would you still be excited doing that work? (Useful for choosing between advisors)
A: It depends on your interests, and I will bring this up front if we are recruiting students with specific background for a grant.
“If you run out of your primary funding for a student how do you expect the student to handle that” (advisor’s responsibility / you’ll have to write a grant with me / dept will cover the student / you have to find their own funding)
A: In general, it's advisor’s responsibility. But as a fallback, the department can also cover the student with TAship.
“What do the quals process and graduation process look like”?
A: The department has a very detailed graduate policy manual.
“Is there a TA requirement”? / “How often would I be expected to TA”?
A: I am not aware if there is a TA requirement from the university or the department. But I do strongly recommend doing a TA in your first or second year. Teaching is a great way to learn the fundamentals.
[If you are interested] Would the advisor be interested in co-advising?
A: Sure thing. If co-advising helps your research, why not.
“Are you taking a student” / “Do you have funding to take students in this year (or, for which projects)”
A: We always are looking for new students each year. I plan to recruit 2-3 PhD students starting Fall 2023.
“What factors will affect whether or not you take a student”?
A: Research interests, background, and motivation.
“How do you anticipate your funding to change during my time as a student”?
A: I don't anticipate any changes. Your time as a student will be fully funded either by research assistantship or teaching assistantship (if you wish).
“Do you think our research interests are a good match”?
A: Let me know what you like to work on.
“Are there other students in my cohort that you’re interested in working with”? (If so, are you taking more than one student?)
A: Not yet. I plan to recruit 2-3 PhD students this year.
“I’m interested in working with you. Do you think I’d have a good chance of working with you if I come to your university”?
A: I would be more than happy to chat! If you come, we can work on a small-scale project and see if this is what you are looking for. In the meantime, feel free to reach out to other faculty and explore different opportunities.
How much overlap would they have in research?
A: Ask me and I will let you know.
Have these advisors co-advised in the past? Or worked together in the past?
A: I am currently co-advising two students with one other faculty.
How often do grad students get to attend conferences? (Pace + What constraints)
A: I support all the graduate students in my lab to attend conferences.
Do students mostly work with senior students or directly with professor?
A: Mostly directly with me, but you are encouraged to collaborate with senior students with similar interests.
How many conferences are students expected to target a year? (remember pubs ≠ submissions)
A: One. Either NeurIPS/ICLR/ICML.
How often do students take time off? Are there lab / department outings/events?
A: Time off: Whenever you feel like you need it. Lab dinner/gathering: Usually once/twice a semester.
Will it be acceptable/encouraged to intern at a company during the summer? (Does this change with seniority?)
A: Yes, it's highly encouraged to do a summer internship!
Do students often work late? (Often / only before conference deadlines.)
A: Mostly before conference deadline, but I would imagine that some students also work late often.
How often are students expected to be contactable by their advisor. (Email, slack, hangouts. Online around the clock?)
A: If I don't know you are off the grid or busy with courses or other obligations, I would probably expect that you are contactable in a day via slack.
Note: Some answers and template refer to Dr. Huang's advisor guide and Dr. Fu's orientation