Ask HN: CS Researchers, why did you choose your subfield and niche?

My advice: don't worry too much about picking a field (and definitely don't focus too much about what people who happen to be the same age as you are up to -- it's a big world).

'elonmollusc's advice is really sound: at least at the beginning, choose the advisor or collaborators, not the subfield. You can probably get yourself interested in any problem if you put effort into it and enjoy the work or the people you're working with. And even if you don't enjoy it, you'll learn something about your own preferences and what kind of environment and style of work you want to build around yourself. Is there a class you've taken where you really enjoyed the material or the professor's style? Go to their office hours and talk to them about research. It's even better if you bring some of your own ideas for what you might be excited to work on (perhaps keying off some of the professor's own recent work; do you have an idea for a way to extend it or a new place to try it?) -- faculty love it when students are a source of new ideas.

If you want to do grad school in CS, here is some stock advice:

- Read some of the essays that people have written about "what grad school in CS is like." I did an interview on this with my friend Eugene Wu (http://www.pgbovine.net/PhD-interview-eugene-wu-keith-winste...), and our friend Adam Marcus (http://marcua.net/writing/gradschool-guide/) also wrote a guide after finishing his PhD. Our other friend Phil Guo also wrote a depressing book about his (not-so-good, but also not horrible) time in grad school at Stanford (http://pgbovine.net/PhD-memoir.htm), and he ultimately ended up in a great job where he seems happy and productive (and now tenured) too.

- Read some of the advice for grad-school applicants, e.g. Michael Ernst's (https://homes.cs.washington.edu/~mernst/advice/) and Jennifer Rexford's (https://www.cs.princeton.edu/~jrex/advice.html) and Justine Sherry's (http://www.eecs.berkeley.edu/~justine/advice.pdf)

- Think real carefully about "Why do you want to go to grad school?" Do you want to use computational thinking to help people in a tangible way? Or do you want to learn about computer science for its own sake, as a quest for knowledge? Do you think you would enjoy a teaching-focused job, or an industrial research job, and these require a Ph.D.? All of these can be great answers, but they are different. What projects or independent activities have you done (things nobody told you to do) that you enjoyed or found satisfying? How can you aim to best continue that? (By contrast, "I did well in undergrad and would like to continue my education" is not a good reason to start a Ph.D. in computer science!)

- Consider taking time off between undergrad and grad school, or at least deferring for a year after you are accepted. Depending on the subfield, the people that do this often have a real leg up, because they can bring a nontraditional perspective to the table. There's little benefit in being "smarter" than everybody else in the group if all that means is that you are saying something first that somebody else was going to say anyway 90 seconds later. It's better to be the one contributing ideas (about new problems worthy of attack OR ways to solve them) or a point of view that wasn't going to get contributed at all. And the people who have exposed themselves to more diverse environments often have had more time to understand themselves and what kind of environment they need to build around themselves (advising, style of work) to be happy and productive. Depending on your advisor's style and the culture of the department, grad school can be almost a totally unstructured and self-directed environment (quite different from undergrad) so you really don't want to go into it as a sort of default. (If you are saying to yourself, "but I don't know what I would do during that year off," that is not a good reason to make grad school become your default! By contrast, if you are saying, "but I am in theory or another mathematically inclined subfield and literally the only way to get better at it is to keep doing it; there is nothing the outside world has to offer me," then maybe you are right -- theorists often do seem to adhere to this kind of thinking.)

- Start thinking about how to demonstrate (by December of the year when you plan to apply) to your letter writers that you have the initiative/resourcefulness/creativity/grit and can see a project through to completion even over obstacles. (Ideally, by participating in a research project that submits a paper for publication.) The letters of recommendation do matter a lot, probably more than anything else in the application, and the letters will ideally be from people that the readers can trust are well-calibrated to speak to your promise at doing CS research.

I'm a CS professor at a primarily-undergraduate U.S. college. I supervise two to four independent research projects or honors theses every year and have published papers and posters based on undergraduate research with my students as co-authors.

If you're going to pursue undergrad research, I don't recommend choosing based on field, but based on potential supervisors. Who are your favorite professors? What subjects have you really enjoyed and would like to explore further? A good project is at the intersection of what you're interested in, what your faculty mentor can realistically supervise, and what you can complete and bring to a good result in 1-2 years. You're going to get the best experience if you're able to work with a faculty mentor you like that can teach you about the research process and devote time to mentoring you. Those skills, more so than any specific project you complete, are what will help you be successful in grad school.

There are probably examples of successful researchers who started in one area as undergrads and stayed with it for decades, but it's more common to switch as your interests change and you get more exposure to different subareas of CS, particularly once you get into a grad program and get more exposure to the open problems in different fields. Grad schools will not require you to stick with the area or theme of research that you may have been pursuing when they accepted you.

To give you a concrete example: my first undergrad research project was writing a simulator for acoustic reverb. I continued working with the same advisor, but we switched to projects related to imaging and remote sensing, which led to experimenting with weird neural network architectures (before the current boom in deep learning really took off) and their applications to multispectral imaging. When I started my Ph.D. program, I was originally working on nonlinear optimization and its applications to sensors, but ended up switching to performance modeling and queueing theory, which became the subject of my dissertation. My research has evolved even more since I became a faculty member and shifted to working with real undergrads; I've recently been doing a lot of qualitative research with community organizations about their uses of data and analytics, which is only tangentially related to traditional disciplinary CS research.

I'd be a little careful with your one-line dismissals of fields. Each field has hundreds of people dedicating their entire lives into it. It'd be offensive to them that someone who hasn't even done any research to think they can judge an entire field like that. Imagine a high school student who took a community college course and saying "University juniors don't know anything. I discovered that they just copy from Wikipedia." The advice in this thread is a great starting point.

I'm a lecturer (assistant professor) in computer science at University of Kent in the UK. I did my undergraduate degree and Masters at Charles University in Czechia and later a PhD in Cambridge. Most of my work has been on programming languages.

How did I choose? Mostly by accident and I definitely did not plan this much in advance. I enjoyed web programming during my undergrad. I got interested in how to make web programming easier (and if you could write both client-side and server-side code in the same language - in the pre-Node.js and pre-transpiler days :-)) and I stared working on a project to translate .NET code to JavaScript. Through a few random lucky accidents, I ended up working with F# and met Don Syme from Microsoft Research. This got me the idea that I could actually do PhD! Through working with Don, I got interested in functional programming and PL research more generally and then applied for PhD with some ideas about reactive programming.

I have no idea how to choose a field or plan a career (I only have one data point!) In my case, it was a mix of doing things I found interesting (without thinking about why this might be useful) and meeting a fantastic mentor at the right moment.

In any case, good luck, no matter what field you end up choosing!

It’s ok to change. It’ll evolve as you get more experience and exposure. It’s not that uncommon to change areas/advisors even a few years into a PhD.

I wanted to go into PL and compilers until I met the prof who later became my PhD advisor. He introduced me to HCI, which still let me dive into some PL topics too!

The research field chooses you. Who do you have access to that influences your perspective on the world. I had professors in undergrad who focused on cryptography and security. I did several independent studies in cryptography. That led me to work at a company that worked in cryptography, and programming language design. the job I was put on at that company had me working on programming language design for machine learning. My friend started working in physical oceanography, and gained access to large amounts of acoustic ocean monitoring data. at the same time my dad started asking questions about how to measure light pollution from photographs, pushing me into image processing. I decided to go back to school for machine learning in order to sort out that data. I focused on identifying whale songs and measuring light pollution, pushing me into machine learning for signal processing. Now I work in machine learning research for identifying security vulnerabilities in compiled binaries. My side projects research now is in natural language processing and information retrieval on medical texts. This is because the graduate school I chose happened to be a medical University.

Because I liked the people in my department doing research in my niche! Unfortunately federal funding in the US disappeared almost overnight for my slice of CS, so I'm currently operating and publishing in a cross-disciplinary fashion in an industry research scientist position. I'm considering leaving the US for a faculty position to get back to the research I really enjoy... so don't do what I did. :)

I chose to focus on computational biology because I had an amazing professor that I wanted to work with. That being said, an amazing professor != amazing advisor... something I discovered when trying to finish my PhD and he decided to take a sabbatical to Kenya to do missionary work :(.

I got enough statistics and machine learning experience in graduate school to be doing machine learning research now as part of the defense industry. Even if machine learning seems like it might be too hot to touch, it will aid you in any number of jobs you could have in the future: automation, data science, etc. It really is perhaps the easiest sub field to cross disciplines. I've always been a PC gamer and the TD-Gammon paper really pushed me forward in my desire to do machine learning -- check it out, its a classic (https://www.bkgm.com/articles/tesauro/tdl.html).

Echoing what a lot of other posters are saying, I would strongly recommend considering advisor/group fit. Of course, research field also matters; it seems like you don't really like networking/security that much, so feel free to steer clear of those disciplines. However, let's say you meet an awesome AI prof, and their work is of moderate interest - that seems like a recipe for a good time, even if the field is saturated/there's an AI winter in our future.

Anecdotally, my first research position was the result of getting my last-choice internship for that summer. I thought I would hate it and was planning to quit after the summer. My advisor was incredible, and I ended up working in that lab for 3 years. That field (human-computer interaction) is now my primary discipline!

Not CS but I went to a lot of seminars and then picked the topic and advisor based on that.

>It continues to amaze me how some people know exactly what they want to do in their freshman year or even earlier.

It's pretty easy. People try things out and if they don't like it, they pick something else. When someone appears to know what they want it is because they did the experimentation phase before they chose their field. It looks like you have the "experimentation phase" still ahead of you and there is no shame in that.

I do security R&D. I chose security in uni because it sounded sexy. Honestly, you sound like a pessimist. I don't work with networking often and only read papers I'm interested in.

Same for neural nets, I could rattle off 3 idea that no one is working on. I don't think the field is saturated.

Just look at what you enjoy doing and specialize in that. You don't need to mixmax the decision and try to predict the future.

Don't be pessimistic you will always find your spot no matter what. Just follow your interest. Review again the papers and research your seniors do and see which one interests you most. Choose something that you like to do all day.

Unified Communications security. I needed to work in a project for multi vendor interoperability. Security in Unified communications was a novel field back in 2010, now is taking off again.

Pick what you can read and enjoy.