Simplifying and Not Trying to Achieve Everything at Once

When is having a good awareness of the "big picture" in a PhD or general research not helpful?

When you need to focus in on the "little picture", to preserve your sanity, lower your sky high stress levels, and make some concrete, tangible progress forwards.

When things get tough in research, researchers, including PhD students, can have a (natural) tendency to try to focus on trying to achieve too many things at once, with the natural result of achieving nothing at all, and getting ever more frustrated as a result.

For research fields like robotics, computer vision and machine learning: yes the eventual aim of many PhDs is to create a new method or set of techniques that improves the state-of-the-art in some meaningful way, including demonstration across many datasets / domains / robots and in the case of "helper" pre-processing / post-processing techniques, potentially across many different techniques.

But when you're struggling, trying to tick all those boxes simultaneously: that novelty box, that generality box - both for domains and for techniques - is completely overwhelming, and usually counterproductive to both your sanity and your progress.

What I often do with students in these situations is reset: dial back the ambition, come up with a simple proof-of-concept of the proposed ideas, often using old statistical or ancient machine learning techniques, and demonstrate that it makes a meaningful difference on <just-one-dataset>.

That won't in of itself be the research they need, the contributions they need, or enable them to publish (probably), but it will be a massive confidence booster and provide them with a rock solid, well understood foundation to build on, rather than floundering in a sea of too many conflicting goals.

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