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 am a PhD as well. Solid advice picking the right supervisor. That is one of the most critical factors to your success (be it publishing, having a career as an academic or getting into grad school in your situation). Don't ask me how to pick the right advisor because I think it is partly fortune, and partly the fit for you. Key thing is to find something you are excited about (and so is the advisor).
Btw .. three pieces of random advice:
- Go to the best grad school possible. Below a certain threshold, it is pointless.
- Learn to write well, and learn to read research papers in your field of interest.
- Get good taste in your problem to solve. When I was starting out my post-PhD life, a very senior Director/VP-level scientist at IBM Research asked me if I had a good taste in picking problems. I gave a BS answer since this was during my job search/interview loop. But I have thought back to that question a lot as I hit mid-career. I suggest you watch the first keynote at MobiSys 2020 (free on youtube). I think you need to pick problems that are within your capability and then build on the skills you have picked up to work on the next better problem, and so on ... all perhaps in sight of a worthy problem at the very end or on the road. I wish I gave this deep thought early in my career .. maybe I'd be more satisfied in my career (but hey .. I still have time .. maybe I'll figure it out one day!).
Not a CS researcher but I would say that advisor is extremely important, but also within the field you are interested in. A good advisor can win you over only so much - there are people who switch advisors because of research topic reasons too (not just because they feel abused). Though if you are in the topic you want to continue in, a good advisor can make a huge, huge difference in terms of getting the most out of you and also promoting you among his /her peers.
To some extent I would not choose based on current market trends - if you want to go to grad school, you're looking at a job market many years down the road (esp. if you do a PhD). However, "saturation" is an issue and this could be demotivating. You definitely don't want to choose a boring topic. If "Bioinformatic/quantum computing" is interesting to you but you're not sure of its applicability/potential but you find it interesting enough to study, I would say this is the perfect field for you to study. Maybe a "deep learning" type revolution is around the corner - or maybe you're going to be part of making it happen.
Not to say market trends are unimportant, but as elonmollusc says, you can end up changing your field of research later - so you should some extent study what interests you most at each moment in time.
Great advice. My undergrad thesis had nothing to do with my master’s thesis, which had nothing to do with my PhD thesis, and now my current job is straying farther and farther away from my grad school work. But the skills I learned by doing semi-independent research, coming up with ideas, solving new problems and what I learned from my advisors are what go me through all of that.
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.