Having effective research meetings

Maya Cakmak

WINTER 2024

CSE 492 R: UNDERGRADUATE RESEARCH IN COMPUTER SCIENCE & ENGINEERING

Why have research meetings at all?

• Discuss things, get on the same page
• Written communication has its limits
• Brainstorm
• Build on each other’s ideas
• To create accountability

Why should you care?

• The most “face time” you will get with faculty:
• Your opportunity to get help and learn from them
• Your opportunity to make a positive impression
• Faculty recommendation letters often reference research meetings
• “Always comes prepared to research meetings”
• “I learn something new in every research meeting”
• “Surprised me with something I did not expect”

Maya’s favorite meetings

• I know what’s coming
• I am not in charge
• I learn something
• I get to share my wisdom
• I get to think deeply
• I get to be creative
• I don’t have any TODOs afterwards
• ..or rather, I have clear TODOs and they are written somewhere ;)

Research meeting advise

“The students who make the best use of meetings tend to have: (1) an agenda beforehand; (2) minutes afterwards; (3) something focused and concrete to discuss/think about/talk about; (4) a consolidated place to keep minutes. Your advisor can read these minutes to prepare for the upcoming meeting, think about problems offline, review/think about the problem outside of meetings, and guide progress. Sometimes your advisor may take notes, sometimes not. Don’t count on it. Even if your advisor is taking notes, your notes will complement and fill in gaps. Different people remember different things. Taking notes is also an important opportunity to practice writing — and students need to practice writing at every opportunity.”

How to do Great Research by Feamster

Research meeting advice

• Be proactive: initiate the meeting
• Prepare an agenda or a list of topics to discuss
• If you’d like an advisor to review written material, plan to give your advisor ample time to review the material
• Aim to have “next steps” clarified by the end of your meeting
• Summarize meeting points, outcomes and next steps
• If and when you are comfortable, share personal circumstances that may be affecting or will affect your academic milestones

Graduate School Guide by Brown University

Potential agenda items

• Progress: What are you doing? How are you doing?
• Specific help and feedback: What specific input and feedback do you need, by when, in what format?
• Trouble points and challenges: Can your advisor help or find resource for you?
• Academic/Professional Plans: Where are you going? Are you on track? Are there issues and concerns you have? Are you thinking of publishing? Are you ready to publish?
• Expectations and next steps for the next meeting

Graduate School Guide by Brown University

Research meeting advise

• Holding regular meetings
• Planning your agenda before the meeting
• Taking notes
• Keeping a research notebook or journal
• ➔ Meeting notes
• ➔ Calculations you have done and lemmas/theorems you have proved
• ➔ Short- and long-term goals and timelines
• ➔ Lists of papers you need to read
• ➔ Ideas for future research
• ➔ Questions you want to resolve
• ➔ Feedback
• ➔ Topics you need or want to learn
• ➔ Relevant conference deadlines and submission plans

Allen School guide: How to get good advising: information about the grad student and advisor relationship

Potential agenda items

• Recap of the goals and state of the project
• An update on what you’ve accomplished since the previous meeting and which previous goals have been met
• Papers you have read or talks you have attended that are relevant to your research and what you learned from them
• Problem-solving strategies
• Gaps in your knowledge or advice on what you should be learning in the short and longer term
• Feedback on progress
• Longer-term goals
• Summary of takeaways and action items going forward
• List of goals you want to accomplish before the next meeting

Allen School guide: How to get good advising: information about the grad student and advisor relationship

Snapshot of a project

Overarching goals of the project

Last meeting

Project start

This meeting

Next meeting

Upcoming deadline

Progress so far

Vectoring in Research

• The temptation is to try and solve the problem that’s set in front of you. Don’t.
• Vectoring is a process of identifying the dimension of highest impact+uncertainty, and prioritizing that dimension while scaffolding the others
• Successful vectoring enables you to rapidly hone in on the core insight of your research project

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

Iteration >> Planning

• Ideas rarely land exactly where you expect they will. It’s best to test the most critical assumptions quickly, so that you can understand whether your hunch will play out, and what problems are worth spending time solving vs. kludging.
• Human creative work is best in a loop of reflection and iteration. Vectoring is a way to make sure you’re getting the most iteration cycles.

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

Vectoring in Research

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

Vectoring in Research - Pick one vector

• The more dimensions there are, the harder gradient descent becomes.
• Instead of trying to do everything at once (project spec), pick one dimension of uncertainty — one vector — and focus on reducing its risk and uncertainty.
• Scope your vector to be something you can reduce uncertainty on in 1–2 weeks

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

Vectoring in Research - Example vectors

• Piloting: will this technique work at all? To answer this, we implement a basic version of the technique and mock in the data and other test harness elements.
• Engineering: will this technique work with a realistic workload? To answer this, we need to engineer a test harness.
• Proving: does the limit exist that I suspect does? To answer this, we start by writing a proof for a simpler case.
• Design: what might this interaction look like to an end user? To answer this, we create a low-fi prototype.

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

Vectoring in Research - Assumption Mapping

• Assumption mapping is a strategy for articulating questions and ranking them.

Stanford CS 197 C - Vectoring in Research by Michael Bernstein

More advice

• UW College of Education - How to Have Productive & Successful Meetings with Your Faculty Advise