[kqr]

All three are problems with performance on a von Neumann machine. So there are three main reasons why Haskell is not a great language for expressing high-performance computation algorithms. Since CS is a branch of maths, performance isn't that big a deal; the important thing is that the result is computable. The performance may be an interesting property, but so is line count and purity and ease of writing proofs and doing analysis on the code!

Speaking more pragmatically, outside of CS study, most of the programs I write do not require me to write high-performance algorithms – they tend to be very I/O bound, and call out to external libraries/services for the data crunching. When I need performance (because of course I do at times), there is an amazing library that lets me write C code in Haskell[1]. The fact that some sections of my program is performance critical doesn't mean I have to write the entire program in C. I can write just the performance critical parts in C.

[1]: https://github.com/fpco/inline-c/blob/master/README.md

[cousin_it]

Um, CS is totally about performance. Frigging P vs NP is about performance. Sure, there's a part that deals with computability and the halting problem, but I'm not sure Haskell is especially useful for studying that either! It can't even express the notion of "dovetailing over all possible programs" very easily :-)

Out of curiosity, what kind of CS do you mostly work with? If it falls under the umbrella of Curry-Howard, then sure, I agree with you. But CS has tons of other areas like graphics, crypto, AI... The impact of Curry-Howard related ideas on these areas has been disappointingly small, IMO.