A quick thread for PhD admits thinking about potential advisors:
I see a lot of discussion about "hands-on" vs "hands-off" advisors
But I think there are at least 3 underlying dimensions here, each of which is worth considering in its own right:
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1) Directiveness—how much your advisor directs your research, in terms of the problems you work on or day-to-day activities
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Low directiveness can mean lots of freedom and the space to think big and chart your own path. However, it can also leave some feeling adrift or unproductive.
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High directiveness can keep you accountable and set you on a good course, especially early in the PhD, but can limit your freedom and feel micromanagey or stifling
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2) Granularity—at what level of detail your adviser engages with you and your research
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Low granularity can mean lots of high-level direction and guidance on vision, but can also be challenging early in the PhD if you need help with experimental methods, proofs, or feedback on writing
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High granularity means your advisor can help unblock you more often and contribute in more ways
But too much can make you dependent, robbing you of the chance to discover your own techniques or the skills for unblocking yourself
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3) Engagement—how much time and energy does your advisor give you?
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Low engagement can work well if you prefer a more independent journey, but can be lonely and leave you feeling like you've fallen through the cracks
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High engagement can lead to lots of learning and growth, but can be stressful or overwhelming, especially if it breaches boundaries or bleeds into nights and weekends
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It goes without saying that these aren't immutable categories—advisors, as well as students, change over time
But hopefully these are some useful tools to help folks consider what's important to them in a mentor
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Hope this thread was helpful, and please feel free to share your own thoughts, whether as an admit, student, or advisor!
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Love the "data science maturity levels" in @Patterns_CP
Interesting way to contextualize research at a glance (reminds me a bit of @justsaysinmice)
Full list in thread:
1) Concept
Basic principles of a new data science output observed and reported (e.g., statement of principles, dataset, new algorithm, new theoretical concept, theoretical system infrastructure)
2) Proof-of-concept
Data science output has been formulated, implemented, and tested for one domain/problem (e.g., dataset with rich domain-specific metadata, algorithm coded up as software, principles with expanded guidance on how to implement them)
Some takeaways from @openai's impressive recent progress, including GPT-3, CLIP, and DALL·E:
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1) The raw power of dataset design.
These models aren't radically new in their architecture or training algorithm
Instead, their impressive quality is largely due to careful training at scale of existing models on large, diverse datasets that OpenAI designed and collected.
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Why does diverse data matter? Robustness.
Can't generalize out-of-domain? You might be able to make most things in-domain by training on the internet
But this power comes w/ a price: the internet has some extremely dark corners (and these datasets have been kept private)
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