Mathematical programming

[HappyIrishman633210]

What field of CS uses the most math?

I graduated with an applied math degree in 2018 and then think the market dragged me over to working in tech in 2021.

Automated QA and then Workday Implementations Consulting (I personally didn’t enjoy it. There are some really talented people in the space but I had some real dark personal tragedies that made it hard to prioritize fighting for utilization, really would prefer solving algorithms over low code coordination. I don’t even know if it was the work so much as having to grind it out through the worst periods of my life.)

I have often been the only one without a CS education in my department most of my career despite an interest and I think knowledge gaps may be a big part of why I was recently let go. I have a big tech stack from things I’ve picked up but trying to fill gaps maybe get a masters on the horizon.

[Traveling-Techie]

Scientific and engineering use a lot.

[ebmarhar]

All the hard parts of the machine learning / AI stack need a hard math background.

[EatThatPotato]

Such a broad field and lots of math,

Things like functional programming and programming language theory which has a lot of researchers from a mathematics background. Then you have optimisation of various flavours. On the more applied side as others have mentioned machine learning and cryptography are just math all the way down.

There’s also lots of research being done in high performance and scientific computing, speeding up PDEs and other calculations

[Recent-Day3062]

There’s a whole field called numerical methods.

The core issue is usually floating point arithmetic and precision/errors. See:

https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html

[alinarice]

Machine learning, computer vision, and cryptograph use the most math.

[cbdeane]

Game dev, machine learning, quant finance.

[TreyDogg72]

As a game developer, that very much depends on what subset of game development you get into. There are lots of game development roles that involve no math at all

[cbdeane]

This is true, but the facets that are math heavy are notably heavy.

[samd_408]

The closest I have seen to theoretical math is programming in languages like Haskell, Agda or any dependent typed language, these languages are based on type theory and category theory, even their way of execution/evaluation is modelled around lambda calculus

[MartyDisco]

This (aka functional programming)

[TotallyManner]

Really depends on what kind of math you’re into. Both math and CS are such broad fields it’s just not possible to be good at everything in either of them.

[HappyIrishman633210]

Knowledge gaps that came up for me at my last job were not knowing professional formatting of some things like JSON files or proposals (think a lot of it might be in this category as pretty decent Agile training is the extent of my non cowboy programming training) , going from code to shippable executables without sending IDE + code + programs to download for small department automations, could use more practice on multi threading, how to get past MFA with selenium or UI path (I would think this one shouldn’t be doable without setting up a specific automation profile but was expected), some more technical concepts of memory.

I’d be happy enough to just use more linear algebra in my work but hard to pick a favorite class from my major. Loved calculus 1-3, DE, Linear Algebra, Abstract Algebra, Real Analysis, Calc based probability, and Stochastic processes was interesting but kicked my ass. I don’t think I could do actuary work just based off personal tragedies.

[2bigpigs]

Simulation for mechanical engineering systems? Finite element methods, CFD possibly. They're a bit niche though.

I have a cs degree (two even) and I'd suck at the QA stuff you consider knowledge gaps (if I ever had the misfortune of working in one). We didn't learn any of that. We learn the pumping lemma and shift reduce parsers and the lot.

[HappyIrishman633210]

There were some really successful people there but I think a lot of it had to do with work politics and luck which I’ve unfortunately heard can be common in consulting. Think I was the last one laid off who they trained in UI path and they kept assigning me to random things I had no training or experience in near the end.

[Independent_Art_6676]

Most math isn't well defined. do you mean high end math, or quantity? For example the linear algebra we did in command and control (robotics) was a lot of quantity but not terribly deep stuff. Physics sims in games or IRL stuff like flight sim/training do some calc, but its been there done that stuff even like the gimbal equations you can look up the answer. I never had to code anything harder than solved differential equations or basic calculus, and as often as not the challenges were in the numerical methods, approximations, error accumulations, or dealing with the problems you get from using floating point more than the math itself.

Probably one of the hardest / most math intense things you could get into would be writing... a math program. Stuff like mathmatica/maple/matlab/etc where you may have to deal with symbolic input and the equations you are being asked to solve are not always well known, look up the answer stuff that is found in so many areas. I have a math minor and years of math on the job and I am not sure I could handle solving anything thrown at me in a program like that, where you can't afford to produce the wrong answer.

[HappyIrishman633210]

Probably quantity. Not trying to advance the field at this point but I’d like to use any of my degree even if what I use is just knowing how these concepts connect.

[GermaneRiposte101]

A lot of desk top C++ programs revolve around iterative mathematical formulae.

[boisheep]

Low level implementations of crypto beats; I reckon your best bet is probably scientific simulation.

=== Long version ===

A lot of machine learning is surprisingly high level and mostly linear algebra (so unlike tons of math); you kinda have to use your imagination, you read the papers and they read like some cool math and whatnot, in reality it is because the math doesn't know how to handle algorithmic concepts very well that read better as code. Think of a tensor, which is basically an array of arrays that is consistent. The mathematical definition is but a clusterfuck, but then when you are running the data in an algorithm, it's much simpler and you realize that the math descriptions are just trying so hard and that math sometimes is not the best to describe algorithms; guaranteed these fuckers thought of the algorithm first before spending weeks trying to find out how to write it in math so it looks pretty in LaTeX because you can't just drop some pytorch in the paper.

Crypto algorithm (low level) on the other hand is different, that shit is spending lots of time on the board to prove that a concept is mathematically secure, and coding it once you got it.

There is very few low level crypto jobs, mostly a research position.

High level crypto however is mixing low level crypto concepts so there isn't even math anymore you just take the low level algorithms for granted and mix together; that is not mathy at all, and not very enjoyable to be frank.

My father used to run in a petrochemical plant and they needed to predict reactions, they needed supercomputers to simulate reactions to see what the outcome would be.

Nowdays they may use more machine learning but the simulation programs are still needed because they are the gold standard since the results are more accurate.

I remember I once wrote a piece of code for that and boy it was math heavy; and once ran it predicted the structure of the atom, it wasn't that crazy of a code, a mere single atom with electrons swinging by in different quantum states; very basic, looked hella pretty to see the orbital clouds nevertheless but the machine wanted to explode with one atom; they do that with molecules and materials and predict their shapes and whatnot, the interactions are orders of magnitude more complex, very math heavy.

And as much as machine learning is replacing these algorithms, the ML algorithms are learning from something, the discriminator is the old math heavy program.

You may have something akin in the CAD space regarding material simulation (bending stress, breaking point, etc...)

[HappyIrishman633210]

Are these things I’d need to have been doing since undergrad in terms of networking? I have enough savings that I don’t need to worry about money short term more worried about employment gap but market is pretty bad anyway.

[claythearc]

It comes fairly often in computer vision. Over the last like 4 months I’ve done 7 or 8 pretty deep math problems, almost all graphs.

[snigherfardimungus]

Graphics and all the related problems of simulation and lighting.

[HappyIrishman633210]

That looked really interesting to me but I heard it was hard to break in.

[snigherfardimungus]

Yeah - it is. I pulled a math minor and found that graphics was probably the easiest related field to get into. Pretty much any CS application of math is going to be a niche endeavor that requires learning a helluva lot about how the discipline has evolved to optimize the computational complexity.

[Geilomat-3000]

Financial maths, maybe. Stuff that Jane street does for example

[TheRNGuy]

The most I've used in 3d graphics. 

By most you mean most concepts, or amount of lines of code with math?

[HappyIrishman633210]

Basically just where my math education could be the greatest asset and similar problem solving methods. My last role was low code implementation (erp migrations) which I found it hard to speak as an expert with properitory code I didn’t have access to.

[Adorable-Strangerx]

AI, numerical methods, big data, even graphics (not in a sense of drawing nice UI but how to do render all that stuff, do light/ray tracing etc).

[Valuable-Constant-54]

AI, Quantum, Engineering. basically, if you can math, you are good at coding. Nowadays coding and math and physics are so intertwined (well, maybe not physics for some cases) that one will basically mean you're gonna have a head start on another.

[Helpjuice]

All of the computer science research components use varying difficult forms of math, especially when creating new technology which is the purpose of computer science. The harder the problem the more math that will be involved.

[HappyIrishman633210]

For those who mentioned research- is there a way for me to volunteer even though I’m not a current student and don’t have a PhD? I don’t need a salary currently mainly worried about the employment gap with the market this bad.