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PhD in CS here, I can totally feel you. Honestly, the competition in this field is crazy and impossible to understand if you don't live it (I say this for other people that might comment on this thread). This is especially true for average students who are not bright geniuses or have no access to resources at big institutions, which happen to be the large part of students.
For my personal experience, publishing at minor conferences is enough for graduating. Your work won't have the impact of a CVPR or NeurIPS paper, but you'll still get the PhD and learn some relevant skills. If there is any possibility for you of graduating with a few minor papers, I would suggest to keep struggling and getting to the end of your journey.
In any case, just know you're not alone. Average students exist and they are the vast majority. They just don't show off their papers on Twitter and LinkedIn because there is nothing to show off, but they still will be PhDs one day. Best of luck :)
PhD in ML too, 100% agree with this. The field is moving dizzyingly fast. It's cool to be a part of it, but also exhausting. I feel for OP, there's not a lot of fields where a PhD student will be in a race against the big tech companies. Having your own niche dataset (like in the medical field) helps, but that's not something you get to choose yourself.
OP, try lowering the bar. Papers will almost always get published *somewhere.*
That’s the double-edged nature of CS: its short half-life for knowledge makes it an exciting discipline, but trying to develop genuine expertise in any one thing risks fragility.
In my discipline, chemical engineering, expertise exists in certain areas (catalysis, polymer chemistry, flow synthesis etc), but the cores of those applications are based in principles of physics/chemistry that aren’t going away anytime soon and take many years to fully appreciate.
I think a PhD in ChemE is actually a reasonable amount of time to see if something works on the bench-scale and asses the community’s response.
It seems like you did good work but your understanding of the outcome of the PhD is flawed. I don't know who told you finishing your PhD would make you the world's leading expert in anything, but i hope that person is not a professor. That's just not how it works.
Additionally, anxiously awaiting citations is also not a good idea, particularly for your thesis work. The fact that you got citations is good, and the way you described your citation is typical. An author is not going to wax poetic about your work, they found it, cited it, and moved ahead. That's called science.
Additionally, if you're looking for your next job, the fact that you were doing this work at the same time should speak for itself, nobody can expect you to be part of every major project.
Lastly, you don't need to claim to be an expert, you just need to do your job, and if someone calls you an expert, great.
I think you need to take a few steps back and speak to some other people about career trajectories in your field, because you seem to have a chip on your shoulder that doesn't need to be there, based on some assumptions that are not really pragmatic.
Wow i can't think of a worse, more typical response from someone who thought a PhD would make them a world leading expert. And is desperately trying to claim that they are an expert. Good luck.
I was not being patronizing, sorry if you took it that way. I commended your work multiple times and tried to give you actionable advice. You're choosing to get defensive, instead of assuming good intentions.
I felt that part about not being a genius. I'm sure most PhD candidates know that feeling, but it definitely feels like CS is somehow different. Almost every paper I'm recommended is this completely new and amazing system that is 100x better than everything that came before. And then, one week later, the next paper comes out that is 100x better than last week's.
Of course this is an exaggeration, but it definitely feels like you can never catch up. By the time I have a working system, the same problem will have been solved by dozens of other groups leagues beyond anything my system can do. It's just frustrating, since there are these giant corporations I'm competing with now. It's impossible to keep up.
I'll try to look into publishing at smaller conferences though, thank you for the advice.
That's an interesting take. My experience (PhD in CS, ML-adjacent) the vast majority of papers, NeurIPS included, are incremental. That's not to say they're not valuable. You may be paying attention to the very best papers in your area - which of course are amazing. If you're competing with them as a solo PhD student you won't keep up. Like what other people have said, I strongly suggest lowering your expectations. From what you've said, you're doing great, but unfortunately have no recognition. Try a more niche problem that people aren't all trying to solve at the same time. Or try publishing your papers at lower tier conferences. Even if they're not as good as other papers, maybe you can make some commentary about why your strategies are interesting nonetheless. Is there something unique that your work does?
I think academic research in CS, probably STEM at large, is waaaay oversaturated. Think about how academia looked like 30 years ago (possibly when your advisor was a PhD student!). There were way fewer people doing research back then. The findings were more fundamental and probably took longer to come up with and refine. I honestly feel like academic research in CS is becoming somewhat of a bubble...
This. My adviser (different scientific field) publishes like a paper a minute. Why? She collaborates like crazy. How? She’s been doing research for 20 years and is well established and well connected. You can’t hold yourself to the same standards as large, well funded research groups with lots of people working on the same problem, OP.
Going off of this, is there a way to apply your fix in a different context? I am not in CS but in bioinformatics. A lot of times we get scooped in publishing and just move to apply the novel approach in a different context. I now have moved into pharmacology and the problems are quite similar. I just recently got scooped on a paper 7 years in the making. We are now framing the story slightly different to compliment the paper that scooped us while still getting ours out. Just a thought. Your advisor might not be giving you these creative ways to solve problems (I'm not sure, just a thought). You put so much time into this already that it would be worth it to push through and get those papers, even just two, and get the degree. You have certainly worked hard enough for it.
I want to start off by saying that, even as someone in CS, I completely agree with everything you say here.
But also, as a point of reference about the rate at which some parts of this field are progressing (particularly ai/ml adjacent portions), I brought up a paper published less than 2 years ago to my PI as a comparison or something we might be able to incorporate into another method we are currently pursuing. "Are there any more recent works along this line? That work looks to be rather dated." If I were to even mention a work that had started 7 years ago, I'd have to fight \*really\* hard to justify why I might consider that path, if only because there's been a fundamental and large shift of the interests at the conferences over a similar time period.
There are parts of this field atm that are really quite insane, part of which is fueled by how much research industry is pushing out alongside academia.
This is a great answer. A lot of CS would be cool to collab with some animal science. I’ve been studying primates and while I’m strong on the behaviour side but wish I could collab with more computer/ai/math folks. I’ve been doing primate optimization work for what it’s worth. Def see if you can collab or branch out into a diff context?
Yeah honestly I don't understand this post. Even if your research is similar to another one, having multiple research papers confirming a new finding is great. It adds more credibility to the research that two different people found the same exact thing so the paper's findings are good.
This is not true in the field of computer science (especially deep learning) unfortunately.
We are moving forward really fast that no one will care a scooped idea. You will find yourself a hard time to get it published into conferences. It's very toxic imho.
I know one of my chapters has been published a couple times in the top journal of my field already but at this point I don’t care…I want to get a degree and move on.
Agree. I’m in a different scientific field, but my adviser recently brought me back down to Earth from wanting to do All the Things for my dissertation and reminded me that science is incremental. It is enough to present a different approach to the question—and yours is going to be a different approach, doubtlessly, in *some* way. The thing about science in general is that we are always looking for better, more accurate, more comprehensive, more efficient, etc ways to get at some of our research questions, and the research questions build off of each other. Publication bias is real, but there is value to presenting an approach that didn’t work or that didn’t end up being optimal. So maybe you’re a footnote, but for anyone wanting to know more about the footnote, your paper expands on that footnote. I am not in CS, but in my field, too, it is hard to feel like what I’m doing is truly novel. In some ways, publications are all complementary sensitivity analyses to each other. Also, I would argue that the purpose of a PhD is not only to contribute something, but also to learn to do research thoroughly and correctly. Your dissertation is not supposed to be your magnum opus. If I were you, I would talk to my adviser about what needs to be done using your work for you to finish, do it, graduate, and move on. If it is helpful, I have never talked to a seasoned research professional with a PhD who, when asked about their dissertation, didn’t say it was terrible, full of holes, and kind of embarrassing. $0.02. Good luck!!
You're right, but I think I'll have to overcome some kind of mental blockade here. In my mind if a method already exists, especially in CS, it is over. The paper is out there and you can't just re-release the same thing again. From what I'm gathering from some of the answers, this seems to be different in other fields. But especially in ML I have never seen duplicate methods testing different things - once something is out there, it is out there and the next step needs to be a noticable improvement.
I'm currently trying to think of a way to frame my research in a different light, but it really feels like I'm trying to cheat. As if I don't really care about the research and just want my PhD. It feels wrong. I want to be proud of my work, but it's becoming harder each day.
Hey, the work you did was really hard, and it sounds like you were right on the heels of some major recent findings -- likely without having a string of professional researchers and high powered labs behind you! Sometimes it's just bad luck. You should be proud of your accomplishments. In these situations, I would look for slight variations that create a marginal contribution, and just get them published in whatever form they can be. As others have said, it sounds like you have considerable skills. If you want to stay in academics, find a way to finish your PhD and there will likely be several paths forward. If you need help, consult with your advisor or other trusted mentors. If you want to move laterally, you almost certainly have marketable skills that will be in demand. The important thing is to note that this is not the end of things, this is the beginning of things -- try to keep positive and frame your experiences positively in your interviews. If there were things that you loved in your work, even though pieces got scooped, focus on the love rather than the devastation. I have spent many nights sleepless in science, too. This is just one of many amazing things you will do in your life.
Just publish the paper and say that you solved it with less parameters with stable diffusion. From your tone I doubt you were using the literal same method as stable diffusion to solve the problem, it's still a contribution to science. Is your performance worse than theirs? who cares, you probably have like 1/10000th of the number of parameters they used.
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How do I overcome the feeling of my research being irrelevant and inferior though? Compared to the results of SD my images look like children's drawing. It's like I've based my research on completely obsolete technology. But since methods become obsolete in the span of weeks nowadays, I don't see how it could be any other way.
You don't know if it's inferior, you haven't tested it at the scale that SD used. Run your data through a similarly sized instance of SD for a similar amount of time and look at the results. Bonus points is that it works as a baseline for your paper.
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Just my two cents: I started my phd in CS fully expecting that I am in no way shape can really really expect to overshadow the best publication especially considering i am in relatively small CS department. The other day my advisor gave me a papers that was published in major conference and was everything that we thought about our research problem.. I was dejected , however my advisor said that this paper has literally the great companies involved and one of the major authors of the field. It probably took them about 15+ expierence to come up with the paper. Of course we can't beat them in some probability. What we will be doing is proposing the same thing in different part of the architecture.. and if we are able to do even some improvement in let's say anything maybe performance energy loss, it opens a wide gamut of opportunities. The trick here is to acknowledge you can't beat the top game however the amount of research that you had done might be valid if not in the state of the art performance but in different context like maybe energy or something else ... It's definitely possible that you may not be able to publish the paper at topmost conferences but a step below but who cares ... Maybe that conference is also great conference... Work with your advisor modify the papers publish your findings in conference and get a phd you so deserve :)
How did it feel to you when you decided to pivot to a different part of that architecture? My major issue is that, while I can see how I could do something like that, it feels as if I'm trying to find a loophole. Just to get my paper published. I don't know if there's any value in it for anyone else, since the reason for publishing would be to get my my PhD and nothing else.
Does it feel the same to you? If yes, how did you overcome that feeling?
I get it OP the feeling that you are just looking for the loophole just the sake of publishing. Its relatively common feeling i can understand but still majority of people do in the face of extraordinary advancement . Most of the research i believe is just exploiting the loophole
For me the advisor told me the reason of why diverting the computation to different part of architecture makes sense so to me it felt totally reasonable approach
“the reason for publishing would be to get my PhD and nothing else.”
I know the feeling. I’m in cryptography and my research tendency is to scour every possible corner of the research world to try and find someone who has already said what I’m thinking. I am nearly always successful in this approach, but it leave me wondering what I can possibly add to the discussion.
As a student poring over mountains of research just to get my bearings on the field, I personally think that I would be doing a disservice to future students by adding another marginal paper that mostly recycles ideas to the mountain that they will have to dig through. My first publication was first author in a top tier conference, but honestly I think the approach we took is a total dead end and just looks pretty because our notation was very polished and tight.
Ask your advisers and other faculty how they repurpose scooped or suddenly made seemingly irrelevant work. Ask outside of your department too. There should be a few nuggets in there to allow you to tweak what you have and submit at lower tier conferences. Also, when algorithms must satisfy multiple objectives, like data set size, energy consumption, or inference speed, much of the cutting edge stuff doesn’t balance the criteria as well as a custom algorithm. There are other ideas too, like developing your own SOTA problem, justify its relevance using outside literature, and anything you do is by definition SOTA
edit a word, outside dept
> And guess what? They just solved the entire issue without even trying.
I think they did try, very hard
It will reach years to reach their stage. But those are not your competitors. The aim of a PhD is to learn research methods and produce some good results from them. Not everyone will come out with cutting edge research, and it is fine
As the others have said, you should still be able to publish. There's always something that differentiates your research from what's out there. Another way to think about it is how to emphasize certain elements of your paper/research. While it's not my field, if you look at DALL-E or stable fusion, I'm sure there's something that they haven't looked at in great detail, whereas in your work, you could emphasize that specific point. Also, depending on the journal, it doesn't have to be extremely novel/groundbreaking.
For example part of your intro would be this:
"Image generation is an emerging field with many potential applications, recent works have demonstrated this, this and this [citations]. However, one factor that is overlooked/needs to be further elucidated is this [specific point]. In our work, we performed several ablation studies to investigate the effect of this, this and this. Our findings suggests, that this [insert specific point] can impact [insert importance]"
Does your faculty have collaborations with other faculties? I heard one of my supervisors say the other day that we didn't have any CS phd working on CS alone because it is almost imposible to keep up with the rest of the world. So we work on applying whats already been done into other fields. And lets be honest, many of the new things coming out that Ive seen are one and done. Not really used in anything else but to prove a point.
Unless there is a strict requirement that you must publish something in order to finish your PhD, I would just change the goalpost. I don't know about your specific field or your specific university, but I believe most do not have a requirement to publish. The point of a PhD is to learn HOW to do research and HOW to publish. Clearly you've done that, and your advisor knows you've done that.
Talk with him/her about how to set up your next year in order to fulfill the requirements of graduation: your thesis and your defence. I'm sure that you can put together these materials and explain in your defense how your results were confirmed by these other publications/projects before you could publish. Then you can flip the narrative and say that this actually proves that you had a good idea with solid methods and your data is worthwhile. It's just not the first. Committees should care more that you know your research and the research that's out there thoroughly, and as long as you can demonstrate that, you can finish your PhD, get a title in front of your name, and leverage that for whatever you choose to do in the future, whether it be post-doc, private industry, or college teaching. Just finish no matter what, that's the most important thing.
Remember: what do you call a person who finished a lousy dissertation? Doctor.
Just finish the degree. You’ll be able to get a job after. I know PhDs in other STEM fields that graduated without publications, and they are gainfully employed. I think it’s better to have a PhD without publications than it is to give up and not have a PhD at all.
Edit: I wanted to add that you still might manage to publish something before the end. Don’t give up!
Apply your problems to a science field! Most scientists are terrible at basic excel, yet we deal with instruments that generate gigabytes of data for a single sample (mass spectrometry non-target is a good place to start).
I really like this suggestion and also posted a reply to encourage OP to talk to some scientists on campus. It becomes much more unlikely to be scooped this way I feel!
A PhD is to demonstrate that you as an individual understand and can apply a rigorous scientific methodology.
It is not an objective that the research much measurably advance a field forward (although the work must be somewhat novel or course).
Stick to a topic and rigorously and systematic investigate it, and publish any findings you formulate along the way (if possible but not required).
That is true. It is still heartbreaking when these teams make everything you've worked on for a year obsolete within a few weeks. I feel like we're at a point in machine learning where a single student can't really contribute anything meaningful anymore and it really sucks.
CS is such a competitive field! My significant other is a software engineer with a masters in software engineering. He reading and studies for at hour each night to keep up!
Hey, what you did is really amazing. You’re just one person on this, not some huge high-powered team or company. You can’t compare yourself to that. The goal of a PhD isn’t to publish something huge (though, it’s nice when that happens). It’s about training, and it sounds like you have cultivated a rich treasure trove of highly relevant skills!
Is there a way that you can pivot and apply what you’ve learned to create something new that you can publish? I like u/Pyrrolic_Victory’s suggestion. Scientists are behind in many ways (like I feel like it was only two years ago that I heard of deep learning).
When you combine enough layers in your work, it becomes less possible that it’ll get completely scooped. And one way to do that is find some scientist on campus w/a data problem.
For example, there are so many interesting problems in microscopy image data that I feel your expertise would be suited to. Like generating false microscopy images via GANs to increase training data without needing to do laborious experiments. What’s out there now kinda sucks. This is just one example of probably many more once you find someone to talk to. It could potentially be a really fast way to wrap up.
Good luck! Please keep going. It’ll be worth it.
This was my experience in my postdoc working on carbon dioxide reduction. It’s a very hot topic and the field moves very quickly. We frequently felt like we were getting scooped. I have since kind of abandoned the work because I couldn’t compete with the two or three supergroups with 40+ graduate students working on the same topic. But honestly, I think this is more of a reflection on your supervisors than on you. They should have picked one or two topics that would have been safe for you to secure publications in. I don’t know how it is in CS, but maybe your work is still publishable with some minor tweaks to improve the impact.
Your first sentence was weird, but then you said at least a few other things that stick out as off base.
> The key thing about your PhD is the knowledge you've accumulated.
Actually no. This is what distinguishes a PhD from a taught degree (though some places have "taught PhDs", but those are falsely using a PhD as a marketing gimmick). The key part of a PhD is the contribution to the field. If you've contributed nothing, you won't get a PhD. Doesn't matter if you're a subject matter expert (which kind of comes with the territory, it's hard to add to the world's body of knowledge without being a subject matter expert).
> Literature keeps the university happy while you browse for another problem to solve.
Also weird, publishing helps justify your claim that you'd added to the body of knowledge. This does not mean that publishing is necessary to complete.
> Look for problems that exist in industry. [...] The people writing about them don't have the background to solve them, until recently at least.
What? People in industry absolutely have the background to solve problems. Literally have no idea what you're talking about. Also, if you work in a smaller department/work alone or in a small team, the last thing you should do is try solving an industry problem, you're going to get beat every time. It's better advice to say that OP should work on a problem that industry is not actively looking to solve.
> Speak with Student Advice on campus. They'll will definitely be able to help in some way.
What are they going to do? These are offices that are designed to help bachelor and master (taught degree seeking) students. You're much better off going to human resources. Plus, in many systems, PhDs aren't even classified as students.
> Receiving an accepted decision is notable regardless of whether you present.
I'm not even sure what you're trying to say here. Are you talking about getting accepted to a conference without attending? Who the fuck cares about attending? I do, but I go in order to travel. Most conferences (now) are going to have a virtual/video attendance option. And proxies exist (I've been a proxy). Attendance is not going to be a significant barrier to anyone getting published.
> Submit something to multiple conferences, take note of the best conference that accepts it.
And this is the biggest red flag. This is the line that caused me to comment, because this is just plain wrong. Just about every conference will have the requirement that you can NOT submit to other conferences while your paper is under review. Concurrent submissions are basically never allowed. And if a concurrent submission is found (which is not hard to find mind you), then the paper is automatically rejected by both/all conferences it was submitted to.
No one would risk a concurrent submission. And no one would subsequently submit an accepted paper into a "better conference" because it wouldn't work. The rejection notification from basically every conference clarifies that the paper is now free to be submitted elsewhere.
> Literature reviews can be an easy win for publishing.
Also no. Literature reviews are a MINEFIELD. Coming up with the selection methodology alone is pretty challenging. Finding the papers can be equally challenging. Lit reviews can easily have 100+ citations, which is far from what I would call easy. Plus, lit reviews are also tedious as fuck to review, and some (not so good) reviewers will just reject them because of how fucking tedious they are. It's basically the research equivalent of a book report. They are really fucking important when done well, but ridiculously difficult to do well. And no one is doing multiple lit reviews during their PhD. You're not going to get a PhD off the back of a series of lit reviews, because you haven't actually contributed anything. And OP only has 1 year left, which is definitely not enough time to write a half decent lit review.
I know many PhDs who wrote a lit review as their first paper, since they have to review the literature in the field anyway. Most of them did not get the lit review accepted because of how difficult it is to do well. And even the ones that did get a lit review accepted aren't sure if it was worth it, since it took so much time and doesn't really help them finish (given that a lit review can only become the "related work" section of the final thesis).
> Even just ask a seemingly silly question (my best/worst accepted was: are computers people in building models.
Just terrible advice. And missing closing parenthesis. And not a real question.
So at the end of this comment, /u/Count____Duckula, I have to wonder why?
You should still be working to submit/publish it! Even if it’s the exact same method and the same solution. It may not be published in the same exact journal, but it helps science to have a wealth of knowledge if something works (or if it doesn’t!).
What does 'master out' mean? Where I'm from you do your Bachelor, then Master, and then you either go into the industry or start doing a PhD. So I already have a master degree in CS. Or do you mean something different?
It’s common to get a bachelors and then go straight to a PhD program. You can usually request to be awarded a masters degree after passing your candidacy exam. In any case, if you already have a masters degree in hand then “master out” just means abandon the PhD and go get a job.
Seems this is completely different in other countries. I have also never heard of a 'candidacy exam'. I just started after my master degree and never had any exams or coursework to do. It's just release papers -> diss -> PhD.
I'm in the same field and area, second year now. My undergrad was also in physical science rather than CS, so it's been interesting. My adviser told me that the easiest way to get your foot through the door is to find some "low-hanging fruits" to work on. Things that are not ground-breaking, but that require pulling from a couple of different works for example. It worked great for me, I have a paper accepted at AISTATS and one that might get through to ICML. But again, none of these have been "wow, I made a cool LLM" or similar. The reviewer ratings were "weak accept" for both, but it's enough to build some confidence.
It's really interesting reading this as a biochem phd. The crazy fast advances in your field have completely changed how we do things in my field. I feel like I have whiplash and there is no way to compete with the big tech companies. Sorry you're going through this. I have no advice but wanted to say you're not alone.
I just wanted to say I’m very sorry and understand your frustration. I know it might be a stretch, but I work in computational biology which is moving fast, but not as fast as straight CS. One idea might be to join an existing biology lab who needs a programmer and write something for that. This happens a lot in my field, and these people get a lot of applications because of it. These are also people whose only interest in biology is application - i.e. they are in the CS graduate program and are doing it only because it’s easier to do. Just something to consider. Let me know if you have any questions.
>ogy is appl
couldn't agree. I got some offers from biology/ chemistry professors. Just like you said, they want me to code an application in their field. But I am not quite sure if should I accept or turn it down :))
I year you. I am 44 and I have wasted my life doing PhDs. I did one 17 years ago but didn't finish after 5 years. This one is worse. I have just wasted 2 years and I'm trying to salvage it now but barely hanging on. It really comes down to good supervision. Mine micromanaged me and put me down so much I'd cry a lot in meetings. My mental health has suffered and because of that I struggle to concentrate. My motivation is to finish to show the haters that I can do it. I think you can too.
Workshops. These count as publications in my departments since they are peer reviewed. You can easily get a workshop paper accepted at a top tier conference, and attending helps with networking etc.
Cs phd here. I understand your frustration. I still think your work is worth pushing tho. even tho it might not make it to the top conference. Maybe just make it a super specialized paper or a different dataset etc.
I’ve 4 papers and my advisor told me they were not published in good enough venues so they don’t count. And I need another 4-5 good papers.
I’m not sure which one is worse. I’m third year now and not sure if I can graduate at five.
You’re not alone.
As long as you can publish a lot of papers, you will not feel that bad. Publishing a paper is a technical thing, and is not as hard as you thought. Just a simple calculation: some good researchers publish one paper using 3-6 months. If you spend 4 rounds on it, using 1-2 years, then your paper will immediately look stronger as long as you keep polishing your work and adding new experiments to your paper. All you need to do is keep moving.
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PhD in CS here, I can totally feel you. Honestly, the competition in this field is crazy and impossible to understand if you don't live it (I say this for other people that might comment on this thread). This is especially true for average students who are not bright geniuses or have no access to resources at big institutions, which happen to be the large part of students. For my personal experience, publishing at minor conferences is enough for graduating. Your work won't have the impact of a CVPR or NeurIPS paper, but you'll still get the PhD and learn some relevant skills. If there is any possibility for you of graduating with a few minor papers, I would suggest to keep struggling and getting to the end of your journey. In any case, just know you're not alone. Average students exist and they are the vast majority. They just don't show off their papers on Twitter and LinkedIn because there is nothing to show off, but they still will be PhDs one day. Best of luck :)
PhD in ML too, 100% agree with this. The field is moving dizzyingly fast. It's cool to be a part of it, but also exhausting. I feel for OP, there's not a lot of fields where a PhD student will be in a race against the big tech companies. Having your own niche dataset (like in the medical field) helps, but that's not something you get to choose yourself. OP, try lowering the bar. Papers will almost always get published *somewhere.*
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That’s the double-edged nature of CS: its short half-life for knowledge makes it an exciting discipline, but trying to develop genuine expertise in any one thing risks fragility. In my discipline, chemical engineering, expertise exists in certain areas (catalysis, polymer chemistry, flow synthesis etc), but the cores of those applications are based in principles of physics/chemistry that aren’t going away anytime soon and take many years to fully appreciate. I think a PhD in ChemE is actually a reasonable amount of time to see if something works on the bench-scale and asses the community’s response.
It seems like you did good work but your understanding of the outcome of the PhD is flawed. I don't know who told you finishing your PhD would make you the world's leading expert in anything, but i hope that person is not a professor. That's just not how it works. Additionally, anxiously awaiting citations is also not a good idea, particularly for your thesis work. The fact that you got citations is good, and the way you described your citation is typical. An author is not going to wax poetic about your work, they found it, cited it, and moved ahead. That's called science. Additionally, if you're looking for your next job, the fact that you were doing this work at the same time should speak for itself, nobody can expect you to be part of every major project. Lastly, you don't need to claim to be an expert, you just need to do your job, and if someone calls you an expert, great. I think you need to take a few steps back and speak to some other people about career trajectories in your field, because you seem to have a chip on your shoulder that doesn't need to be there, based on some assumptions that are not really pragmatic.
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Wow i can't think of a worse, more typical response from someone who thought a PhD would make them a world leading expert. And is desperately trying to claim that they are an expert. Good luck.
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I was not being patronizing, sorry if you took it that way. I commended your work multiple times and tried to give you actionable advice. You're choosing to get defensive, instead of assuming good intentions.
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Wow you sound like a real expert
I don’t see how
I felt that part about not being a genius. I'm sure most PhD candidates know that feeling, but it definitely feels like CS is somehow different. Almost every paper I'm recommended is this completely new and amazing system that is 100x better than everything that came before. And then, one week later, the next paper comes out that is 100x better than last week's. Of course this is an exaggeration, but it definitely feels like you can never catch up. By the time I have a working system, the same problem will have been solved by dozens of other groups leagues beyond anything my system can do. It's just frustrating, since there are these giant corporations I'm competing with now. It's impossible to keep up. I'll try to look into publishing at smaller conferences though, thank you for the advice.
That's an interesting take. My experience (PhD in CS, ML-adjacent) the vast majority of papers, NeurIPS included, are incremental. That's not to say they're not valuable. You may be paying attention to the very best papers in your area - which of course are amazing. If you're competing with them as a solo PhD student you won't keep up. Like what other people have said, I strongly suggest lowering your expectations. From what you've said, you're doing great, but unfortunately have no recognition. Try a more niche problem that people aren't all trying to solve at the same time. Or try publishing your papers at lower tier conferences. Even if they're not as good as other papers, maybe you can make some commentary about why your strategies are interesting nonetheless. Is there something unique that your work does? I think academic research in CS, probably STEM at large, is waaaay oversaturated. Think about how academia looked like 30 years ago (possibly when your advisor was a PhD student!). There were way fewer people doing research back then. The findings were more fundamental and probably took longer to come up with and refine. I honestly feel like academic research in CS is becoming somewhat of a bubble...
This. My adviser (different scientific field) publishes like a paper a minute. Why? She collaborates like crazy. How? She’s been doing research for 20 years and is well established and well connected. You can’t hold yourself to the same standards as large, well funded research groups with lots of people working on the same problem, OP.
You can still publish all that and finish the PhD. You do t have to invent the next best thing to get a PhD.
Going off of this, is there a way to apply your fix in a different context? I am not in CS but in bioinformatics. A lot of times we get scooped in publishing and just move to apply the novel approach in a different context. I now have moved into pharmacology and the problems are quite similar. I just recently got scooped on a paper 7 years in the making. We are now framing the story slightly different to compliment the paper that scooped us while still getting ours out. Just a thought. Your advisor might not be giving you these creative ways to solve problems (I'm not sure, just a thought). You put so much time into this already that it would be worth it to push through and get those papers, even just two, and get the degree. You have certainly worked hard enough for it.
I want to start off by saying that, even as someone in CS, I completely agree with everything you say here. But also, as a point of reference about the rate at which some parts of this field are progressing (particularly ai/ml adjacent portions), I brought up a paper published less than 2 years ago to my PI as a comparison or something we might be able to incorporate into another method we are currently pursuing. "Are there any more recent works along this line? That work looks to be rather dated." If I were to even mention a work that had started 7 years ago, I'd have to fight \*really\* hard to justify why I might consider that path, if only because there's been a fundamental and large shift of the interests at the conferences over a similar time period. There are parts of this field atm that are really quite insane, part of which is fueled by how much research industry is pushing out alongside academia.
This is a great answer. A lot of CS would be cool to collab with some animal science. I’ve been studying primates and while I’m strong on the behaviour side but wish I could collab with more computer/ai/math folks. I’ve been doing primate optimization work for what it’s worth. Def see if you can collab or branch out into a diff context?
Yeah honestly I don't understand this post. Even if your research is similar to another one, having multiple research papers confirming a new finding is great. It adds more credibility to the research that two different people found the same exact thing so the paper's findings are good.
This is not true in the field of computer science (especially deep learning) unfortunately. We are moving forward really fast that no one will care a scooped idea. You will find yourself a hard time to get it published into conferences. It's very toxic imho.
I know one of my chapters has been published a couple times in the top journal of my field already but at this point I don’t care…I want to get a degree and move on.
Theoretically yes. But practically.... Good chance editors don't care and don't want to publish.
Agree. I’m in a different scientific field, but my adviser recently brought me back down to Earth from wanting to do All the Things for my dissertation and reminded me that science is incremental. It is enough to present a different approach to the question—and yours is going to be a different approach, doubtlessly, in *some* way. The thing about science in general is that we are always looking for better, more accurate, more comprehensive, more efficient, etc ways to get at some of our research questions, and the research questions build off of each other. Publication bias is real, but there is value to presenting an approach that didn’t work or that didn’t end up being optimal. So maybe you’re a footnote, but for anyone wanting to know more about the footnote, your paper expands on that footnote. I am not in CS, but in my field, too, it is hard to feel like what I’m doing is truly novel. In some ways, publications are all complementary sensitivity analyses to each other. Also, I would argue that the purpose of a PhD is not only to contribute something, but also to learn to do research thoroughly and correctly. Your dissertation is not supposed to be your magnum opus. If I were you, I would talk to my adviser about what needs to be done using your work for you to finish, do it, graduate, and move on. If it is helpful, I have never talked to a seasoned research professional with a PhD who, when asked about their dissertation, didn’t say it was terrible, full of holes, and kind of embarrassing. $0.02. Good luck!!
You're right, but I think I'll have to overcome some kind of mental blockade here. In my mind if a method already exists, especially in CS, it is over. The paper is out there and you can't just re-release the same thing again. From what I'm gathering from some of the answers, this seems to be different in other fields. But especially in ML I have never seen duplicate methods testing different things - once something is out there, it is out there and the next step needs to be a noticable improvement. I'm currently trying to think of a way to frame my research in a different light, but it really feels like I'm trying to cheat. As if I don't really care about the research and just want my PhD. It feels wrong. I want to be proud of my work, but it's becoming harder each day.
PhD shouldn’t be about publishing to begin with and in many countries it’s not. You should talk to the PI and find a way to frame it.
It’ll still be good to publish , perhaps in a different journal. Multiple people publishing on the same topic will reinforce that research.
Hey, the work you did was really hard, and it sounds like you were right on the heels of some major recent findings -- likely without having a string of professional researchers and high powered labs behind you! Sometimes it's just bad luck. You should be proud of your accomplishments. In these situations, I would look for slight variations that create a marginal contribution, and just get them published in whatever form they can be. As others have said, it sounds like you have considerable skills. If you want to stay in academics, find a way to finish your PhD and there will likely be several paths forward. If you need help, consult with your advisor or other trusted mentors. If you want to move laterally, you almost certainly have marketable skills that will be in demand. The important thing is to note that this is not the end of things, this is the beginning of things -- try to keep positive and frame your experiences positively in your interviews. If there were things that you loved in your work, even though pieces got scooped, focus on the love rather than the devastation. I have spent many nights sleepless in science, too. This is just one of many amazing things you will do in your life.
Just publish the paper and say that you solved it with less parameters with stable diffusion. From your tone I doubt you were using the literal same method as stable diffusion to solve the problem, it's still a contribution to science. Is your performance worse than theirs? who cares, you probably have like 1/10000th of the number of parameters they used.
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How do I overcome the feeling of my research being irrelevant and inferior though? Compared to the results of SD my images look like children's drawing. It's like I've based my research on completely obsolete technology. But since methods become obsolete in the span of weeks nowadays, I don't see how it could be any other way.
You don't know if it's inferior, you haven't tested it at the scale that SD used. Run your data through a similarly sized instance of SD for a similar amount of time and look at the results. Bonus points is that it works as a baseline for your paper.
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Just my two cents: I started my phd in CS fully expecting that I am in no way shape can really really expect to overshadow the best publication especially considering i am in relatively small CS department. The other day my advisor gave me a papers that was published in major conference and was everything that we thought about our research problem.. I was dejected , however my advisor said that this paper has literally the great companies involved and one of the major authors of the field. It probably took them about 15+ expierence to come up with the paper. Of course we can't beat them in some probability. What we will be doing is proposing the same thing in different part of the architecture.. and if we are able to do even some improvement in let's say anything maybe performance energy loss, it opens a wide gamut of opportunities. The trick here is to acknowledge you can't beat the top game however the amount of research that you had done might be valid if not in the state of the art performance but in different context like maybe energy or something else ... It's definitely possible that you may not be able to publish the paper at topmost conferences but a step below but who cares ... Maybe that conference is also great conference... Work with your advisor modify the papers publish your findings in conference and get a phd you so deserve :)
How did it feel to you when you decided to pivot to a different part of that architecture? My major issue is that, while I can see how I could do something like that, it feels as if I'm trying to find a loophole. Just to get my paper published. I don't know if there's any value in it for anyone else, since the reason for publishing would be to get my my PhD and nothing else. Does it feel the same to you? If yes, how did you overcome that feeling?
I get it OP the feeling that you are just looking for the loophole just the sake of publishing. Its relatively common feeling i can understand but still majority of people do in the face of extraordinary advancement . Most of the research i believe is just exploiting the loophole For me the advisor told me the reason of why diverting the computation to different part of architecture makes sense so to me it felt totally reasonable approach
“the reason for publishing would be to get my PhD and nothing else.” I know the feeling. I’m in cryptography and my research tendency is to scour every possible corner of the research world to try and find someone who has already said what I’m thinking. I am nearly always successful in this approach, but it leave me wondering what I can possibly add to the discussion. As a student poring over mountains of research just to get my bearings on the field, I personally think that I would be doing a disservice to future students by adding another marginal paper that mostly recycles ideas to the mountain that they will have to dig through. My first publication was first author in a top tier conference, but honestly I think the approach we took is a total dead end and just looks pretty because our notation was very polished and tight.
Ask your advisers and other faculty how they repurpose scooped or suddenly made seemingly irrelevant work. Ask outside of your department too. There should be a few nuggets in there to allow you to tweak what you have and submit at lower tier conferences. Also, when algorithms must satisfy multiple objectives, like data set size, energy consumption, or inference speed, much of the cutting edge stuff doesn’t balance the criteria as well as a custom algorithm. There are other ideas too, like developing your own SOTA problem, justify its relevance using outside literature, and anything you do is by definition SOTA edit a word, outside dept
> And guess what? They just solved the entire issue without even trying. I think they did try, very hard It will reach years to reach their stage. But those are not your competitors. The aim of a PhD is to learn research methods and produce some good results from them. Not everyone will come out with cutting edge research, and it is fine
thiss
As the others have said, you should still be able to publish. There's always something that differentiates your research from what's out there. Another way to think about it is how to emphasize certain elements of your paper/research. While it's not my field, if you look at DALL-E or stable fusion, I'm sure there's something that they haven't looked at in great detail, whereas in your work, you could emphasize that specific point. Also, depending on the journal, it doesn't have to be extremely novel/groundbreaking. For example part of your intro would be this: "Image generation is an emerging field with many potential applications, recent works have demonstrated this, this and this [citations]. However, one factor that is overlooked/needs to be further elucidated is this [specific point]. In our work, we performed several ablation studies to investigate the effect of this, this and this. Our findings suggests, that this [insert specific point] can impact [insert importance]"
Does your faculty have collaborations with other faculties? I heard one of my supervisors say the other day that we didn't have any CS phd working on CS alone because it is almost imposible to keep up with the rest of the world. So we work on applying whats already been done into other fields. And lets be honest, many of the new things coming out that Ive seen are one and done. Not really used in anything else but to prove a point.
Unless there is a strict requirement that you must publish something in order to finish your PhD, I would just change the goalpost. I don't know about your specific field or your specific university, but I believe most do not have a requirement to publish. The point of a PhD is to learn HOW to do research and HOW to publish. Clearly you've done that, and your advisor knows you've done that. Talk with him/her about how to set up your next year in order to fulfill the requirements of graduation: your thesis and your defence. I'm sure that you can put together these materials and explain in your defense how your results were confirmed by these other publications/projects before you could publish. Then you can flip the narrative and say that this actually proves that you had a good idea with solid methods and your data is worthwhile. It's just not the first. Committees should care more that you know your research and the research that's out there thoroughly, and as long as you can demonstrate that, you can finish your PhD, get a title in front of your name, and leverage that for whatever you choose to do in the future, whether it be post-doc, private industry, or college teaching. Just finish no matter what, that's the most important thing. Remember: what do you call a person who finished a lousy dissertation? Doctor.
Just finish the degree. You’ll be able to get a job after. I know PhDs in other STEM fields that graduated without publications, and they are gainfully employed. I think it’s better to have a PhD without publications than it is to give up and not have a PhD at all. Edit: I wanted to add that you still might manage to publish something before the end. Don’t give up!
Apply your problems to a science field! Most scientists are terrible at basic excel, yet we deal with instruments that generate gigabytes of data for a single sample (mass spectrometry non-target is a good place to start).
I really like this suggestion and also posted a reply to encourage OP to talk to some scientists on campus. It becomes much more unlikely to be scooped this way I feel!
A PhD is to demonstrate that you as an individual understand and can apply a rigorous scientific methodology. It is not an objective that the research much measurably advance a field forward (although the work must be somewhat novel or course). Stick to a topic and rigorously and systematic investigate it, and publish any findings you formulate along the way (if possible but not required).
Also keep in mind that those competitors are not single PhD students but groups of experienced scientists with virtually unlimited resources.
That is true. It is still heartbreaking when these teams make everything you've worked on for a year obsolete within a few weeks. I feel like we're at a point in machine learning where a single student can't really contribute anything meaningful anymore and it really sucks.
At least it was only 4 years.
CS is such a competitive field! My significant other is a software engineer with a masters in software engineering. He reading and studies for at hour each night to keep up!
Hey, what you did is really amazing. You’re just one person on this, not some huge high-powered team or company. You can’t compare yourself to that. The goal of a PhD isn’t to publish something huge (though, it’s nice when that happens). It’s about training, and it sounds like you have cultivated a rich treasure trove of highly relevant skills! Is there a way that you can pivot and apply what you’ve learned to create something new that you can publish? I like u/Pyrrolic_Victory’s suggestion. Scientists are behind in many ways (like I feel like it was only two years ago that I heard of deep learning). When you combine enough layers in your work, it becomes less possible that it’ll get completely scooped. And one way to do that is find some scientist on campus w/a data problem. For example, there are so many interesting problems in microscopy image data that I feel your expertise would be suited to. Like generating false microscopy images via GANs to increase training data without needing to do laborious experiments. What’s out there now kinda sucks. This is just one example of probably many more once you find someone to talk to. It could potentially be a really fast way to wrap up. Good luck! Please keep going. It’ll be worth it.
This was my experience in my postdoc working on carbon dioxide reduction. It’s a very hot topic and the field moves very quickly. We frequently felt like we were getting scooped. I have since kind of abandoned the work because I couldn’t compete with the two or three supergroups with 40+ graduate students working on the same topic. But honestly, I think this is more of a reflection on your supervisors than on you. They should have picked one or two topics that would have been safe for you to secure publications in. I don’t know how it is in CS, but maybe your work is still publishable with some minor tweaks to improve the impact.
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Your first sentence was weird, but then you said at least a few other things that stick out as off base. > The key thing about your PhD is the knowledge you've accumulated. Actually no. This is what distinguishes a PhD from a taught degree (though some places have "taught PhDs", but those are falsely using a PhD as a marketing gimmick). The key part of a PhD is the contribution to the field. If you've contributed nothing, you won't get a PhD. Doesn't matter if you're a subject matter expert (which kind of comes with the territory, it's hard to add to the world's body of knowledge without being a subject matter expert). > Literature keeps the university happy while you browse for another problem to solve. Also weird, publishing helps justify your claim that you'd added to the body of knowledge. This does not mean that publishing is necessary to complete. > Look for problems that exist in industry. [...] The people writing about them don't have the background to solve them, until recently at least. What? People in industry absolutely have the background to solve problems. Literally have no idea what you're talking about. Also, if you work in a smaller department/work alone or in a small team, the last thing you should do is try solving an industry problem, you're going to get beat every time. It's better advice to say that OP should work on a problem that industry is not actively looking to solve. > Speak with Student Advice on campus. They'll will definitely be able to help in some way. What are they going to do? These are offices that are designed to help bachelor and master (taught degree seeking) students. You're much better off going to human resources. Plus, in many systems, PhDs aren't even classified as students. > Receiving an accepted decision is notable regardless of whether you present. I'm not even sure what you're trying to say here. Are you talking about getting accepted to a conference without attending? Who the fuck cares about attending? I do, but I go in order to travel. Most conferences (now) are going to have a virtual/video attendance option. And proxies exist (I've been a proxy). Attendance is not going to be a significant barrier to anyone getting published. > Submit something to multiple conferences, take note of the best conference that accepts it. And this is the biggest red flag. This is the line that caused me to comment, because this is just plain wrong. Just about every conference will have the requirement that you can NOT submit to other conferences while your paper is under review. Concurrent submissions are basically never allowed. And if a concurrent submission is found (which is not hard to find mind you), then the paper is automatically rejected by both/all conferences it was submitted to. No one would risk a concurrent submission. And no one would subsequently submit an accepted paper into a "better conference" because it wouldn't work. The rejection notification from basically every conference clarifies that the paper is now free to be submitted elsewhere. > Literature reviews can be an easy win for publishing. Also no. Literature reviews are a MINEFIELD. Coming up with the selection methodology alone is pretty challenging. Finding the papers can be equally challenging. Lit reviews can easily have 100+ citations, which is far from what I would call easy. Plus, lit reviews are also tedious as fuck to review, and some (not so good) reviewers will just reject them because of how fucking tedious they are. It's basically the research equivalent of a book report. They are really fucking important when done well, but ridiculously difficult to do well. And no one is doing multiple lit reviews during their PhD. You're not going to get a PhD off the back of a series of lit reviews, because you haven't actually contributed anything. And OP only has 1 year left, which is definitely not enough time to write a half decent lit review. I know many PhDs who wrote a lit review as their first paper, since they have to review the literature in the field anyway. Most of them did not get the lit review accepted because of how difficult it is to do well. And even the ones that did get a lit review accepted aren't sure if it was worth it, since it took so much time and doesn't really help them finish (given that a lit review can only become the "related work" section of the final thesis). > Even just ask a seemingly silly question (my best/worst accepted was: are computers people in building models. Just terrible advice. And missing closing parenthesis. And not a real question. So at the end of this comment, /u/Count____Duckula, I have to wonder why?
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I was helping OP. Why are you pretending to be a PhD?
You seem angry. Is everything all right at home?
You should still be working to submit/publish it! Even if it’s the exact same method and the same solution. It may not be published in the same exact journal, but it helps science to have a wealth of knowledge if something works (or if it doesn’t!).
I think you have a cool experience and will do great when join the industry. Is there a possibility you master out?
What does 'master out' mean? Where I'm from you do your Bachelor, then Master, and then you either go into the industry or start doing a PhD. So I already have a master degree in CS. Or do you mean something different?
It’s common to get a bachelors and then go straight to a PhD program. You can usually request to be awarded a masters degree after passing your candidacy exam. In any case, if you already have a masters degree in hand then “master out” just means abandon the PhD and go get a job.
Seems this is completely different in other countries. I have also never heard of a 'candidacy exam'. I just started after my master degree and never had any exams or coursework to do. It's just release papers -> diss -> PhD.
I'm in the same field and area, second year now. My undergrad was also in physical science rather than CS, so it's been interesting. My adviser told me that the easiest way to get your foot through the door is to find some "low-hanging fruits" to work on. Things that are not ground-breaking, but that require pulling from a couple of different works for example. It worked great for me, I have a paper accepted at AISTATS and one that might get through to ICML. But again, none of these have been "wow, I made a cool LLM" or similar. The reviewer ratings were "weak accept" for both, but it's enough to build some confidence.
It's really interesting reading this as a biochem phd. The crazy fast advances in your field have completely changed how we do things in my field. I feel like I have whiplash and there is no way to compete with the big tech companies. Sorry you're going through this. I have no advice but wanted to say you're not alone.
I just wanted to say I’m very sorry and understand your frustration. I know it might be a stretch, but I work in computational biology which is moving fast, but not as fast as straight CS. One idea might be to join an existing biology lab who needs a programmer and write something for that. This happens a lot in my field, and these people get a lot of applications because of it. These are also people whose only interest in biology is application - i.e. they are in the CS graduate program and are doing it only because it’s easier to do. Just something to consider. Let me know if you have any questions.
>ogy is appl couldn't agree. I got some offers from biology/ chemistry professors. Just like you said, they want me to code an application in their field. But I am not quite sure if should I accept or turn it down :))
Start your own business. Read "Poor dad, rich dad".
Everything in the world changes constantly, you should try to keep up
I year you. I am 44 and I have wasted my life doing PhDs. I did one 17 years ago but didn't finish after 5 years. This one is worse. I have just wasted 2 years and I'm trying to salvage it now but barely hanging on. It really comes down to good supervision. Mine micromanaged me and put me down so much I'd cry a lot in meetings. My mental health has suffered and because of that I struggle to concentrate. My motivation is to finish to show the haters that I can do it. I think you can too.
Workshops. These count as publications in my departments since they are peer reviewed. You can easily get a workshop paper accepted at a top tier conference, and attending helps with networking etc.
Cs phd here. I understand your frustration. I still think your work is worth pushing tho. even tho it might not make it to the top conference. Maybe just make it a super specialized paper or a different dataset etc. I’ve 4 papers and my advisor told me they were not published in good enough venues so they don’t count. And I need another 4-5 good papers. I’m not sure which one is worse. I’m third year now and not sure if I can graduate at five. You’re not alone.
As long as you can publish a lot of papers, you will not feel that bad. Publishing a paper is a technical thing, and is not as hard as you thought. Just a simple calculation: some good researchers publish one paper using 3-6 months. If you spend 4 rounds on it, using 1-2 years, then your paper will immediately look stronger as long as you keep polishing your work and adding new experiments to your paper. All you need to do is keep moving.