I have refined my research taste, found my research momentum and gained more research experience. It’s interesting how much of the previous advice (in my opinion) no longer applied to me. I now compiled a new set of resources for myself that I resonate with or learn more from.
Ethan Perez - Tips for Empirical Alignment Research
Execution: (1) quickly implement a well-scoped idea; (2) run a high volume of experiments–––You’re doing really well here if it’s hard for your supervisor to keep up with the volume of the experiments/results you’re showing; (3) can design a minimal experiment to test a mid/high-level idea; (4) bias heavily towards speed instead of code quality. See further for workflow on methods to try (e.g. zero-shot to RL) and others
Michael Bernstein - Velocity in Research
The only takeaway I need: measure progress by velocity (i.e., how many creative different ideas I’ve tried.) The core-peripheral part isn’t really applicable imo–––we are stuck in the swamp because we could not figure out the core problems.
In preparation for my Computer Science PhD (specializing in NLP) starting at 2021, I compiled these resources–––written or revised after year 2017–––here in an alphabetical order. I think they gave me a good start in my early years of my PhD.
Alex Irpan - The 5 Year Update on Skipping Grad School (and Whether I’d Recommend It)
This post puts the question “What to (really) get out from a PhD program” in perspective by comparing doing research in industry and in an academic program. By knowing the differences, I can focus on opportunities unique to my PhD.
Arun Kumar - The Secret Lives of Millennial CS Assistant Professors (Part 1)
Even though this Medium article targets Assistant Professors as its audience, I benefit a lot from reading about how Arun chooses problems to work on and to collaborate on. I particularly like how he values intellectual independence and takes advantage of academic freedom. Besides, his takes on research dissemination and freedom of speech inspire me to be more outspoken.
Bill Freeman - How to do research
I’m adding this as a third-year PhD student because I think the advice of “in graduate school, it’s the hard workers who pull ahead.” and “you can build up intuition about what matters with simple toy models” really stands the test of time.
Christof Monz - So, you want to do a PhD…
This is one of my most favorite blog posts for the fact that Christof based his advice on the end goal of “finishing the PhD thesis”, so his tips are very actionable. On top of the things to do, he also detailed the situations to anticipate at different stages of PhD education.
Diana R. Cai - Tips for New PhD Students in Machine Learning
This blog post touches on many skills that PhD students need to equip. One thing I like about Diana’s post is its comprehensiveness – it even includes a section about working from home!
Dmytro Mishkin - How to navigate through the ML research information flood