>>13657402>AlgebraAlgebraic complexity is the premier intersection of algebra and CS, and it's gotten a lot of attention from mathematicians recently. Here's a great book that surveys the topic
https://www.math.tamu.edu/~jml/simonsclass.pdf>basic complex analysisAnalytic combinatorics is used extensively in harder algorithms analysis. The idea is that you can analyze the tricky generating functions associated with algorithm runtimes by studying *transfer theorems* that use complex analysis to relate various series to combinatorial "puzzle pieces." Also has a free and extensive survey book free online:
http://algo.inria.fr/flajolet/Publications/book.pdf>AnalysisAnything to do with statistical learning theory, which can be studied anywhere from CS depts to math to stat to physics. Lots of measure theoretic probability theory.
https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12_toc.pdf>PDEsUhh, so basically "computer graphics" is a codename for solving PDEs with tricky boundary conditions. This will touch geometry and analysis as well. Everything in the link below, including the "notes" which are literally just a big monograph on differential geometry and its use in CS/physics, should be useful.
https://cseweb.ucsd.edu/~alchern/teaching/cse274_2020/>Frankly, I'd want that but with more, like, outright maths desu?You'll get a lot of that in a lot of subfields of CS, especially those related to learning theory, many subfields of complexity theory, quantum information theory, etc etc.. But some classic parts of complexity theory are definitely more "definition heavy" and don't seem as "mathy" though this is mostly untrue.
If you want to learn about subfields of CS that use a lot of specific math, I can likely name some directions for you to look at.
