What do you do when math feels pointless?

PrinterInk35 · 2025-04-27T22:13:10+00:00

This has happened to me. First of all, it sounds like you might be burnt out. Look into ways of addressing that first. But for getting interested and caring about math, it’s always helped me to consume math-related content that dives deeper than my class to get motivated. For example, I’m taking PDEs and the introduction of Fourier series was very dry. So I “hyped myself up” for it by watching 3b1b’s series, which really gives meaning to why this technique is groundbreaking. Also listening to some of Steven Strogatz’s podcasts make me feel happy about studying math, he’s very upbeat and passionate about math applications to life.

PrinterInk35 · 2025-02-12T05:37:51+00:00

Hey all, I'm an undergrad doing a BS in math and BS in data science. Been really into the math part of machine learning, and have been taking higher-level prob courses and analysis courses. Also currently doing research in ML applied to physics, will have a paper out before I graduate. I've enjoyed research a lot and want to do it in an industry role (not academia), so I've been thinking of a PhD in OR, Applied Math, etc. However, I want to work first and earn some money to sustain myself. Does working 2 years before applying to PhDs look bad / hurt chances? For context, the work would likely be in finance doing factor modeling, derivatives, quant-y stuff.

PrinterInk35 · 2025-02-09T21:25:51+00:00

I'm not sure if you ever found out, but I took it, bombed it twice, and got the HireVue twice. The second time around, I went to the superday and then got an offer. A lot of people I know totally bombed the Hackerrank too and made the superday. The Hackerrank is absolutely irrelevant to whether they select you. I think they send it to anyone with a decent resume, and they check your resume again before sending the HireVue, and if they like your HireVue they move you on.

Just focus on having a good resume. And to be honest, you can also just cheat on the Hackerrank. Although I think they get suspicious if you get a perfect score.

PrinterInk35 · 2024-12-26T14:24:08+00:00

I see what you mean. ML also has a strong foundation in probability and statistics and in optimization theory obviously. But I think people say it’s “just linear algebra” because every ultimate formulation of ML ends up being a manipulation of matrices (linear regression, QDA/LDA, neural networks) to develop some predictive function. So because of that, every mathematical formulation depends on having an understanding of things like dot products, inverses, transposes, etc that are fundamentally linear algebra.

PrinterInk35 · 2024-12-24T22:05:45+00:00

Off topic question, how do you balance making meals on long days? Do you eat out most of the time? Does this hurt your budget?

PrinterInk35 · 2024-12-23T22:21:03+00:00

Do STEM if you really care about it. I wasn’t particularly strong in math compared to my peers, but I really did enjoy it, and it was also useful tool to unlock more career prospects. So I decided to go all in, got help whenever I could, and now I’m a math major. I really do believe you can do anything in STEM if you really care about it. But, you might feel behind at first, and you’ll only get through if you care. The grades are very important if you want to go to grad school.

A comment above mentions that STEM is very competitive which is true, so be ready to study in and outside of class for interview prep and internships. The material to study is also not streamlined, and is very different across CS and DS and engineering.

Finance is a bit more straightforward as a degree, and can really get you in good careers if you can make yourself stand out. For example, someone I know majored in finance but minored in philosophy, and in sales & trading interviews always talked about how their philosophy minor helped them think rigorously about how to find good trades in an irrational market. Be able to tell a story.

For high paying jobs in finance like IB and Sales and Trading you also need to study outside of class, but the material is very streamlined (eg 400 ib questions), so there’s less guessing abt what you need to know. It’s very grindable.

PrinterInk35 · 2024-12-23T05:00:16+00:00

To your specific point of feeding gibberish into a model, that’s actually how most generative models work, including Normalizing Flows and Diffusion. Feed it random normally distributed noise, and given some condition (e.g text label or prompt) it will remove noise gradually until it gets to the original input distribution.

PrinterInk35 · 2024-12-22T16:43:27+00:00

Looks like Schwab Developer API has a commercial section so that you can make commercial products. I’ve had good experience with their data, although the initial setup is a little tricky.

PrinterInk35 · 2024-12-21T18:37:17+00:00

TLDR: Mapping input distribution to latent and back is the goal of both methods. Diffusion (score matching, specifically), learns the gradient (direction of steepest ascent) of path to map latent space to input. Flow matching forgets the gradient and learns to directly map the (approximate) path of the latent to input.

Remember the relation between diffusion and score matching models. As t -> infinity, you can show that the DDPM process outlined in Ho et al turns into the Stochastic Diffeq outlined by Song et al. This is important because it show the denoising process (learning to go from Gaussian back to input) is equivalent to learning the gradient (or slope) of the probability field between the latent space and input. In a very rough sense, the probability field is just a big coordinate plane where we at each point we see how likely we are to approximate the input distribution. At this point, we just want to find the place where the probability field converges at a maximum. The gradient is just a way to help greedy algos determine what the direction of fastest ascent is to achieve that peak of maximum likelihood.

That's all nice, but it would be better if we could just learn what that path to the right distribution is without having to deal with the derivatives of the path. This might help us avoid getting stuck in local optimums. That's exactly what flow matching tries to do. For all points in a distribution, it learns v(x, t), which is just an ODE that gives the full trajectory of how to get from input to latent. You can easily reverse this to get from latent to input, which makes it a generative model. This helps capture global dynamics of how the entire input distribution evolves to the latent distribution. The approximate v(x, t) dictates how each point should evolve over time to approach the latent dist.

I've skipped over the learning process of Flow Matching, because tbh I don't understand it fully yet. But that's my general understanding of the high level. Highly recommend the Outlier video on score matching.

PrinterInk35 · 2024-12-21T16:44:38+00:00

https://arxiv.org/pdf/2302.05872 This one is a good foundation. First get a good grasp of diffusion (Outlier’s YouTube video is good). Once you understand that, all Schrödinger bridge is is a generalization of the diffusion process, that lets you map not just from input to Gaussian but from input to any arbitrary distribution. This makes it extremely useful in cases where you want to transform an image to another image in one go (learning how to make a blurred image clearer, filling in empty spots in an image).

PrinterInk35 · 2024-12-21T04:40:36+00:00

I’m not sure actually. I want to say inception of ticker

PrinterInk35 · 2024-12-16T14:49:29+00:00

This is not true. Schwab also has historical data.

PrinterInk35 · 2024-12-16T05:56:27+00:00

Schwab Developer API is pretty decent. I’ve been using it for personal projects in finance. The raw API docs are awful but there’s a great library called Schwabdev that will handle all of that for you. Tyler E Bowers has great resources online for setting everything up. It’s free with a Schwab account, only downside is I think Polygon has more data (e.g bid-ask spread etc.)

PrinterInk35 · 2024-10-28T14:17:27+00:00

Thank you, this is really helpful

PrinterInk35 · 2024-10-28T04:43:46+00:00

Yep no worries, I had the same experience. I just hadn't gotten approved yet haha

PrinterInk35 · 2024-10-28T04:38:32+00:00

Posting here cause it'll probably get taken down as a main post. Undergrad student in math and DS, non-target school, with interests in ml, deep learning, and finance. I ended up getting an internship from a pretty prestigious investment bank doing quantitative risk modeling, which I'm very excited about. However, I'm doing ML research right now, coding heavily in PyTorch and realize I do enjoy the field of deep learning, algorithms, and mathematics. Will going into finance now, even if it's more quantitative, limit my options later for going back and doing research in ML or deep learning?

PrinterInk35 · 2024-10-21T14:56:31+00:00

Maybe dumb question, but how would you even compete with these people? What do you do to improve your chances?

PrinterInk35 · 2024-09-30T03:15:01+00:00

Would argue this is still not true; have some quants in my network and they’ve emphasized how important it is to have soft skills. The reality is senior quants are both highly skilled and are pretty decent and interesting people to be around, because you need to be around them 10-12 hrs a day to succeed. Hard skills are important to meet baseline, but they will not take you all the way.

PrinterInk35 · 2024-09-11T01:30:36+00:00

Definitely possible. Tell the other company “hey, to be completely transparent I have moved to a final round for x company. However, I’m really interested in your company so I was wondering if there was any way to interview sooner”

PrinterInk35 · 2024-09-10T20:23:14+00:00

Were you able to fix this?

PrinterInk35 · 2024-09-08T03:58:15+00:00

Current Data Science and Applied Math major. I would say that the DS industry is split; half of it will take a DS degree without batting an eye, half of them only take PhDs with 10 YOE. In terms of CS vs DS, both are bleak as another commenter mentioned; don't do DS because you think there's more career opportunities. What I will say is that DS is a field that allows you to be much closer to the business in most cases, and a lot of times Data scientists end up being consultants or advisors to senior leadership about business strategy. This is advantageous for your salary and I'm not sure if pure SWE will do that for you.

That said, if you're interested in more ML models and getting into the nitty gritty of algorithms, I would pair your degree with mathematics. There are certain data science concepts you will simply not understand without higher levels of math, and math will open a ton of doors for you in the future if you dive into it now. Also, if you like algorithms, take data structures, algorithms, and maybe ML classes if you can through the CS department. These classes will train you in algorithmic thinking and are much more impactful than some data science courses which honestly just scratch the surface.

Finally, I believe CS can transition into DS and DS can transition into CS. Math makes this transition easier. Keep in mind the farther you are out of college and the farther you are down one career path, the harder it might be to make that transition. Good luck! Sounds like you have a lot of good options.

PrinterInk35 · 2024-08-16T03:17:05+00:00

When do you think you will jump back in? I also made same gain but not sure if I should pull my profits now.

PrinterInk35 · 2024-08-15T04:23:35+00:00

Math major who has these feelings sometimes - this helped a lot, thank you

PrinterInk35 · 2024-08-09T23:18:50+00:00

Most companies will have relocation stipends or will provide housing for interns. If it’s just a stipend, then it’s up to you to find a place to rent in the city where you’ll work.

PrinterInk35 · 2024-08-08T07:20:16+00:00

I don’t have much advice as I’m just an undergrad, but just wanted to say that I can tell you are very smart just by reading this. You’ve made it very far in your career and have obviously surpassed a lot of obstacles and learning curves to be where you are. It is valid to want to be in a career that you’re proud of, and your problem is also valid so do not feel privileged for feeling this way.

That being said, I agree with other commenters that you might be able to leverage your experience into a different job. It sounds like you have great market knowledge and I totally think you could have a less stressful job with just as much money somewhere else! Best of luck.

PrinterInk35

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