>>13558327Many areas are, but a decent amount are self contained. It's sort of hard to say. For example, a lot of great work on efficient tabling algorithms can be understood very easily in an isolated context - you get why they're useful and know how they work by studying combinatorics and the CS literature. But a huge share of why they exist and work like they do is down to the computational needs of genetic sequencing where we accepted we were going to take time to do things, but that we can be incredibly space efficient and sometimes cut down pathologically hard cases into annoying but tractable ones. Knowing how bioinformatics pushed algorithmicists is a really nice tidbit.
For other fields like robotics and quantum computing, however, interdisciplinary isn't a choice. You can do work in the "CS corner" for sure, but you have to put out research that is aware of the physics and engineering community's needs.
>logicseen a more than decent amount, though overall niche
>graph theoryused everywhere, obviously. everyone uses the LLL or szemeredi's lemma at some point on random graphs and thresholds for algos
>psychonly if you do cognitive science
>biosee above
>statML/AI, brownian motion seen a lot in random algos, etc
>EE computational imaging and compressed sensing
>lingusticsNLP
>>13558465Theory of computation is a vague categorization of topics that have to do with machine models. It's self contained, but hardly the work of choice for most theoretical computer scientists, much less working ones in the US.
>you don't need advanced knowledge of stat to do TCSuhh depends on what parts of TCS you do. Randomized algorithms needs a fuckton of measure concentration results, where stat and probability matter. On the other hand, graph algos needs nothing but combinatorics.
>read the book 'The Nature of Computation' for freegood book but narrow view of what computer scientists actually study.
