3. Roles & Expectations
The expectations I care about most are consistency and communication. Working steadily and telling me where things stand matters much more to me than raw hours. It’s one of the most useful habits you’ll build during your training, and it keeps deadlines from piling up. Consistent work and open communication also let us iterate, which is how we get to high-quality results.
1:1s and time with me #
We meet weekly for a 1:1, and I try to book them on our shared in-person days (Wednesday and Thursday). The meeting is yours, so don’t cancel it just because progress feels slow. Some weeks we’ll take a walk and talk through early ideas, and other weeks we’ll dig into specifics. If you need more dedicated time, book it through my booking page [TBD].
Coding critique #
I recommend everyone do at least one coding session or code critique, with me or with other people in the lab. It shows you how someone else reads your code. Even with good AI tools, writing code yourself is worth practicing because it trains how you break problems into small, approachable tasks. For why working alongside others helps, see Codemates.
By role #
PhD students. For a classic perspective on what a PhD actually is, see Matt Might’s The illustrated guide to a Ph.D. I expect at least three works that you lead. The first teaches you how to get into the field, the second builds up the difficulty, and the third should be ambitious enough to help define your direction after graduation. One natural way to make a project more ambitious is to connect it to experiments through collaborators. Leading three works across five years is a reasonable baseline, though doing more will help your career.
Master’s students. One work that you lead.
Undergraduates. The aim is a well-scoped project, whether a summer position or a course project. Really, the goal is to give you a sense of what research is. If a publication comes out of it and helps you apply to grad school, that’s great.
Postdocs. Yes, publishing is expected, but you’re much more in control of your career, and the lab is here to support and enable your next step, whether that’s academia or industry. Let’s talk about where you want to go so I know how best to support you.
Growing as a researcher #
You probably won’t know how to propose a project at the start. It’s very common not to have ideas at the beginning; you’re still learning the process. The hope is that by the end of your PhD you’ll know how, and you’ll have tons of ideas. Chris Olah’s research taste exercises are a great way to practice: write down ideas and have a mentor rate them.
Science is open-ended discovery, which means sometimes things don’t work. Open-ended projects carry more risk, and that’s okay.
Mentoring and helping others is expected, but flexible. Some people want to mentor students and some don’t, and that’s fine. It’s always good practice, though: we’ll always be working with people, and learning how to do that well is something worth getting better at.