text:
Is it unwise to pick an old field of research in maths to write a PhD thesis about?
Some areas that come to mind:

theory of partial differential equations (analysis and pdes, not numerical PDE)
number theory
algebra

Should I pick a trendy topic so that it is "easier" to write a thesis?
I've heard from senior PhD students not to try and write a thesis on analysis of PDEs because so much has already been done in this field and so it's very hard to do something new.

Picking a topic for your thesis is something you do together with your advisor. If you have one, talk to them about this. If you don't have one yet, then it is too early to pick a topic.

Since I dispute these areas as "old", it would be interesting to know which fields you do consider "trendy".

What are you trying to achieve? (a) maximise your chance of getting a PhD; (b) maximise the intellectual satisfaction gained from doing it; (c) maximise the impact your research has on the world; (d) maximize your reputation and standing in the academic community (e) do something that no-one else would have even thought of doing; (f) ensure that whatever you do, it leaves you time for partying in the evenings. The answer might depend.

Please also think about the (career) possibilities  a topic will give you after the PhD. Although the PhD is to get you ready for independent research, many adverts for Post-Doc and other academic positions list skills and area's of expertise required for the position. Some PhD topics allow you to gather more "wanted" skills, than others.

@MichaelKay Sure those fields are old. They're still alive and vigorous, but they're old.

@MichaelKay Very good. You should make that into an answer.

You don't write a thesis on algebra; you write a thesis on Foo cohomology of Gok modules of Bizz rings of Wozzles.  When it comes to trendiness - it's on the level of Wozzles being trendy while Wizzles are not.  Also, the successful grad students I know don't pick a single thing and keep working on it; they look at ten problems in a similar subfield, work harder on the five they have some ideas on, work still harder at the one or two they actually make some progress on, and hopefully they solve one problem for a dissertation.

If you struggle to find a topic then you should reconsider the PhD. Want to do something epic? Solve the malleability problem in Bitcoin

@TobiasKildetoft Not true in all situations. Some universities require a research proposal built-in to your PhD application, before you even get assigned an advisor.

@TobiasKildetoft: Also, some advisor insist on a topic for you, without giving you much input into the matter.

you have a chance to pick a thesis topic? isn't it forcefully decided by "advisor"?

If you pick an overly-trendy area there is a chance that it will be easier to get a Ph.D. in that area but that many mathematicians will be less than impressed by the resulting degree. For example, "fuzzy" is (or has been?) a trendy concept. There is almost a template for getting a Ph.D. in it (albeit one which is getting harder to apply by sheer competition): pick some topic in pure mathematics for which there doesn't yet exist a fuzzy version (for an example the exact phrase "fuzzy symmetry group" has only 4 Google hits). Learn the classical theory. Fuzzify it: whenever you see the word "set" replace it with "fuzzy set". Prove something -- which with lack of competition shouldn't be all that hard. Anticipating the question as to why anyone should care, hand-wave about potential applications to artificial intelligence. Voila! You have a dissertation (I am exaggerating a bit, this is of course easier said than done, and a successful fuzzification will involve more than simply replacing sets by fuzzy sets).
Now I don't deny that the core concepts of fuzzy logic and fuzzy set theory have a great deal of mathematical depth, and that much of the fuzzification of classical pure mathematics has been well-motivated. Still, it seems likely that much of it has been done just because it was low-hanging fruit for somebody who wanted to write a dissertation or paper. I am instinctually skeptical of the value of a paper when it contains the word "fuzzy" in the title. I won't dogmatically reject it, but I will need some convincing as to its value. If your dissertation has a trendy buzz-word but otherwise seems unmotivated, you might meet with similar skepticism.

" a successful fuzzification will involve more than simply replacing sets by fuzzy sets " I wish this were true...

@AndrésE.Caicedo I was trying to be diplomatic, although I seem to remember seeing something about fuzzy equivalence relations which was fairly interesting (though maybe that is closer to pure fuzzy set theory than e.g. "fuzzy quasigroups").

Hilarious and helpful answer - thanks Johnnie

Fuzzwords are the new buzzwords.

My brain got all fuzzy reading this. What about fuzzy neuroscience? (Now that I write this I am getting increasingly scared that that might be already a thing.)

@skymningen, neuroscience isn't necessarily fuzzy, but fuzzy psychology ≡ all of psychology... :D

Damn, I could have been a mathematician!

In engineering, replace by "neural networks"

All of the areas you mention are extremely broad areas and extremely popular.  For instance, it doesn't make sense to call number theory a trendy topic or not, except in the sense that there are many trends that come and go within number theory.  Moreover, being "trendy" is essentially independent of being "relatively easy."  Two topics that are sort of trendy in number theory close to me now are p-adic Langlands and beyond endoscopy, both of which are incredibly technical and require a huge amount of background to get into, whereas some other trendy parts of number theory like Apollonian circle packings and Ramanujan graphs are much easier to get into.
Just because a certain subject is more established doesn't mean it's harder to work in.  It just means it's richer and has more subareas to specialize in.
What you should do your thesis is an area that (1) you would be happy working in, and (2) you can find a suitable advisor for.  In any case, easy should not be a reason for doing a PhD (most people will tell you it is not easy---mine turned out to be, but I got lucky).
Note: there are certain areas that have fallen out of favor, and it can be harder to get an academic research position if you work in one of these areas, unless you can connect it to things people are interested in nowadays.  However, there's a big difference between not being trendy and being comatose.

I'm going to say some stuff based on my own Physics PhD. Disclaimer: These are just my opinions and are undoubtedly affected by my cynicism.
PhDs are not all created equal
I'm of the belief that PhDs are not equally difficult. Additionally, PhDs are not equally valuable. In fact, value does not necessarily correlate with difficulty. I knew students who were given projects that turned out to be gifts - a simple idea with simple execution that produced papers like a gold mine. Some PhD projects are absolutely easier than others so don't let anyone persuade you otherwise.
Myself, I got landed with something that was borderline impossible (professors from other labs expressed their sympathies when I described my work) and, though I eventually succeeded, trying to get scientific value out of it was like trying to get blood out of a stone.
Here's my advice:

Comprise a list of all the current 'sexy' on-trend topics in your field
Choose the one that most genuinely interests you

That's it. My justification is as follows:

Point 1 increases your ceiling for success (incremental improvements to old methods won't skyrocket your career), point 2 increases your chances for success (because you'll remain enthusiastic and see it through when the going gets tough)
On-trend subjects get a lot of attention at conferences. Attention leads to corporate and government interest, which in turn leads to grants. I've got to tell you that, however much you enjoy your PhD project, you'll enjoy it a lot more with increased funding.
On-trend subjects produce material that'll make your posters and presentations attract other academics like bees to honey. Networking is absolutely vital in academia and choosing a on-trend subject will net you a lot of powerful friends.
You might have an easier time getting published if your research field is truly cutting edge. I published several papers detailing improvements to existing scientific methods, but I don't mind telling you that it was an uphill struggle to sell the idea to the reviewers.
I don't see the problem in choosing an on-trend subject to make your life a significantly easier, providing that you're actually interested in that subject
If your field actually takes off, your name will be among those few who pioneered the research. You don't need me to tell you how valuable and fulfilling that would be.

Hope that helps.

Your question alludes to the interesting notion that research in pure math is analogous to investing in stocks (or other financial assets), whereby choosing your research area is akin to choosing which stock to invest in. The idea is that a savvy researcher will cleverly choose the research areas with the highest expected "return on investment" to invest their time in: some research areas are supposedly old and dead, with all the exciting results having been discovered a long time ago so that it is difficult to make any meaningful contributions; while other more recently founded areas are so new and exciting that many "golden" results are metaphorically lying in the streets, just waiting to be picked up by anyone walking by without requiring great talent to uncover. Your question reflects your desire to "pick a winner" and achieve greater success than others who are not so careful to optimize their picks of research area.
Now, how accurate is this analogy? Can one really improve one's odds of success in pure math through such strategizing in the same way that this is possible (though far from easy) to do in the stock market? Well, one school of thought would say that the efficient market hypothesis  guarantees that the job market for academic math positions, like the stock market, is efficient, with all the other "players" having already made choices that guarantee that any field one enters is as difficult to produce good work in as any other: new and exciting fields will quickly become so crowded and competitive that one might as well find some less trendy niche research area where you can work by yourself and the lack of competition compensates for the decreased fruitfulness of the subject.
I would argue differently however. I think it's obvious to any experienced mathematician that the "math research market" is not an efficient market (neither is the stock market, incidentally, which is why some investors are consistently successful in making more money than everyone else there) and that it is possible to "pick winners". In fact, the ability to pick winners and invest one's time and effort in fruitful research directions is precisely one of the things that separates successful mathematicians from less successful ones; this ability is part of what we call "talent" (the other part of talent is the talent required to actually solve research problems once you have decided which topic to think about, of course).
After this somewhat abstract discussion, let's get back to your question. I don't think it's unreasonable of you to try to optimize your choice of research area for career success -- as I said, all good mathematicians do this -- but your question does reflect a certain naivete regarding the scale at which this optimization takes place. Each of the areas "number theory", "algebra" and "PDE" that you mention represents such a huge part of mathematics that it is meaningless to ask whether the area is a wise one to do one's research in. This makes no more sense than asking "is it unwise for me to do my PhD in math rather than computer science because math is an old field of research?" As others have said, your actual research topic will be much more specialized than "math" or "computer science" or "number theory" or "algebra". Some research subfields of any of these fields are indeed no longer very productive or trendy, while others are thriving (as a small example, in the "old" area of number theory there has been some incredibly exciting progress recently towards the twin prime conjecture thanks to the work of Yitang Zhang and the follow-up Polymath8 project). The way the optimization actually happens is at a much smaller scale: within very particular research subfields, some researchers are consistently good at asking the right questions, or identifying connections between two seemingly unrelated subfields and making a clever choice to invest their time in thinking hard about both subfields to dig deep into that connection.
Another way in which I see your question as being naive is that you are not taking your natural skills and talents into account. Doing successful research in different areas of math requires hugely different skills. Indeed, I think it's quite rare for anyone to simultaneously consider specializing in either algebra or PDEs, since those are very different areas which require different abilities and tendencies (I find the notion that you are entertaining both simultaneously to be rather amusing). What would be unwise is for you to pick a research area without having any feeling that your particular problem solving skills and other talents are in some way adapted to the area you are picking -- some sense that you are drawn to the field, that you have a good intuitive feeling for it, are good at solving problems in it, and -- ideally -- find it especially interesting, are passionate about it, etc.
To summarize, I think the general idea of considering carefully which research subjects you want to work on in order to maximize your chances of success is a sound one; but it's not really helpful to do this according to broad, simplistic measures of old versus new or trendy versus stale. The best approach is to combine getting advice from multiple experienced people with listening to your own voice regarding which areas you are drawn to and think your talents are suited for, and which problems your intuition tells you are exciting and good research directions to go in. Good luck!

Yes, and/but, to take the investment analogy further, having a good advisor is like having serious insider information in the stock market. There, it's not legal. For beginning mathematicians, it is legal, and probably necessary. :)

It is not always that you can just "pick an area". The research has to fit with the project that your supervisor has gotten grants for. (S)He can't just ignore what he has promised the financers to research. Given that you are employed by him and tied to a project ( not always the case ), then you better do stuff related to the project or it may look bad for the supervisor for using the money allocated for something to do something else.

Self-funding is Always an option in such a case.

@WeckarE. I find your definition somewhat sloppy.

You are listing areas of work, not topics.  You do research within an area, not about an area, so your question title is somewhat misleading.  In many ways, your question is analogous to a neuroscience Ph.D. student choosing to do Ph.D. research on "the brain" as opposed to "the spinal cord".  To someone in the field you're studying, your question sounds just as naive.  People have been doing research on the brain since ancient Egypt, and this will continue for centuries to come.
For all the areas you've mentioned, there are many questions that remain to be answered.  In conjuction with your mentors, you will pick a topic within the broad area.

Think about your core idea(s) and do some research into the current state of play, what has been done in the field, and when.
Something/someone that springs to mind is that (Dr.) Brian May of Queen fame was awarded his PhD in 2007 after starting it in 1971. A lot of research can be done in 36 years, but in his case the topic hadn't been explored in the interim.
In the end it is you who will be spending the next 3-4 years on it.

A few thoughts:
Pick a topic you care about.  The sort of thing you'd pursue as a hobby because you want to know.  You'll need the motivation.
Received wisdom is always worth revisiting, especially as new data and new tools for analysis and simulation come in
What about doing something 'meta' - if so much has been done with these analyses, do methods fall into categories that themselves are worthy of study?

Lot of great answers. But I just want to add one thing that my supervisor told me long ago.
No field is old enough that you cannot do research on it. You just
need to dig deeper, ask the right set of questions.
As you know that we do not know everything about anything. So just dump this idea that I should pick a trendy topic. Choose whatever you like (and you can find a supervisor for it), as long as you are asking right questions, every field is worth researching.

Nevertheless, the point of getting a PhD is to enable one to do future research (in terms of having both the skills and the job prospects), and that depends on getting enough results and publications to continue one's academic career. Focusing on a moribund or dead field is not conducive to that goal.

Of course number theory and algebra are old areas - both of them date back at least to the Babylonians (hence are at least 3000 years old). Just because something is old it doesn't mean that it is bad. It could also mean that those areas are worthwhile. They were already old 100 years ago. You should look at how number theory was done 100 years ago and how it is done now. Some questions are the similar, but many new ones emerged (for instance in connection with algebraic geometry), and the new ones are as exciting as the old ones.
I think it is wiser for a PhD student to focus on the new ones. This is because the old ones are most likely very hard (if they are interesting, a lot of intelligent people have thought about them and failed).  Also, it likely that more people will study the new problems (for the reasons I just mentioned), which means that there are more people for you to talk to (more collaborators) and which might be able to inspire you.

This question does not define its terms (what exactly is "trendy"?, for instance), leading me to suspect it is an unserious query that is close to trolling.
Even so, I'll say that the point of entering a Ph.D. program is to learn how to conduct research and to convey your findings to different audiences, not simply to earn a degree.
If "getting the Ph.D." is your motivation, get out now and do something else that makes you reasonably happy--whether that's coding or running a coffeehouse or needlepoint.
Trust me, there are easier ways to make a living despite the many benefits that accrue to you once you earn the doctorate.
The point of a doctoral dissertation is to conduct a sustained research project that, among the many other arguments that it must make, must demonstrate the significance of its chosen topic(s) and your project's approach(es) to said topic(s).
Otherwise, what's the point? Why go to the trouble?
Finding a topic about which you are passionate and that can meaningfully occupy 2-4 years of your attention is necessary. Trends are just that: trends that ebb and flow. What's "hot" when you begin may be out of fashion by the time you complete the final draft.
You can always survey what's being published and taught in your field(s) of study to determine what's popular at the moment, but a good project makes its own space within its field(s) and subfield(s), so chasing after trends may simply lead to misery rather than enlightenment (or even entertainment).

If your motivation is "to get a Ph.D." rather than "to research XYZ" - you should probably not start a Ph.D. program
One of the most common reasons for the stress, depression and feelings of failure that I've encountered as a graduate student union official is people who start a Ph.D. without clear motivations - or with the wrong motivation.
If you're so uncommitted to anything that you're thinking "Oh, I might pick this field, or that field, etc." - then it's not your time to start. And - getting a Ph.D. should not be a goal unto itself, but rather just a byproduct of doing useful research work.

It's not just about motivations. Many people who start simply don't know what it means to be a PhD student. Once you learn what it means you are already in and have made a commitment. You could be super good and motivated at a field of study but fail some other of the necessary things to graduate.

No, because whatever you do is based on your interest. While doing PhD on the subject you are interested in you need to think before that whether you have passion towards it or not. Unless you show some passion towards your theises or the field you select to move on Even the latest researches are also useless.
My best suggestion to you is that dont ask someone while declaring your research field since you are some passion towards your field you need to think only one thing that how to develop your research in your field. Now go on thinking with a small paper in your hand and let your heart talk to your brain.
And have a good look over all the fields that you are loving and choose something as the best one.
Unless you decide on your own about something you cant succed Since when you start hating it you will blame someone but if you select it on your own YOU CAN EVEN MAKE GOLD OUT OF COAL..

Can you explain clearly what you are trying to say instead of the philosophical jargon?


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My Supervisor is not an expert
I just started my PhD which is in Artificial Intelligence (AI).
My supervisor is specialized in Electrical Engineering with a hint of AI.
He knows that it is not his area of expertise and suggested if I can find a mentor who is more specialized.
I contacted many professors from other universities but no one accepted to become my mentor.
I'm not sure what to do now, especially because it is not easy to find another university.
Do you have any advice or suggestion for this situation?

Why did you choose a supervisor that is not an expert in AI? I suspect no one will be willing to supervise unless there is a formal arrangement or that someone is an existing collaborator of your supervisor.  Your best option is focus on an EE project.

AI is an innately inter-disciplinary topic. When AI is applied to a real problem, it is just as important (if not more important) to have a domain expert who know the problem inside out as it is to an AI expert. As long as the project isn't deriving fundamentally new methods in AI, but rather applying AI to some domain, then a primary supervisor in the problem domain and a secondary in ML is perfectly suitable. However, the primary supervisor should be the one finding the secondary supervisor.

@Prof.SantaClaus Coz this is the university that accepted my PhD application, I applied for other Unis but with no much luck, this Dr. accepted my proposal and he told me it will be hard but will be able to do it. now I am listening to the comments here, I am really scared now.

@IanSudbery My PhD is in ML, and my supervisor is not expert in ML :(

I bet you are in Australia,  where according to my observation,  this is very very very common. I was in this situation myself, along with a bunch of my phd colleagues.  What I did: contacted somebody in Europe who kindly agreed to unofficially supervise me and steer me towards finishing. You need to have a good profile for anyone to be willing to invest the time in you though.

@anucex I am looking for someone to do so & yes i am in australia

I would ask for clarification on this line:

He knows that it is not his area of expertise and suggested if I can find a mentor who is more specialized.

What does he mean by this? Is he willing/eager to supervise you, or not?

If not, then I agree with the other answers here, find a different supervisor at all costs
But if he is willing to supervise you (and he said this just to warn you that he is not an AI expert), then you could try to make this work. You'll have to agree on a schedule.

I recommend that you spend the first year to teach yourself about AI (maybe check out pyimagesearch.com -- the paid materials are excellent, though overpriced...I assume you know how to code already, if not, that will be a problem), get your supervisor to send you to a conference like CVPR or NIPS, and take any courses your university offers. You should cover all the basics like CNNs, LSTMs, and GANs as well as non-DL techniques (e.g., random forest, hidden markov models, etc.). Not everyone has what it takes to keep motivated and teach themselves a new subject for a whole year; hopefully you do (if not, consider switching universities)
From there, your EE supervisor should be able to help you identify a research topic of mutual interest -- for example, running these under SWaP conditions, or proving information theoretic theorems, or whatever else EEs care about, I don't know.

It really depends on you. If you have good background, have done Masters and knows how to to do research, done some AI experiments, determined and motivated, yes you can do Phd. with your described circumstances. You can design an experiment and submit papers and from there you build your knowledge.
Many students do Phd. in almost totally independent way. Like anything in life, there is an existing ideal image drawn about what/how things should be done. But you find yourself in another circumstances that this ideal image can not be applied.
My rule is: "Do what you can, with what you have, where you are".
You can either:
1) Change the topic/area to something that your supervisor can provide expertise.
2) Continue in what you want, validate your results through conferences.
Start from what you know and keep going.

You face a few options. Suppose that you insist that you must do your PhD in AI.

You should do whatever it takes to find a new advisor or an appropriate co-advisor. This includes talking to other faculty, the department chair, and even the dean of the college and graduate school. When "whatever it takes" fails to be a suitable choice, you must take a different approach.

Suppose that you decide or determine that doing a PhD in AI is not as important as doing a PhD at the university where you currently reside.

You should do whatever it takes to find an advisor + project that is of suitable interest to you for your work. This especially includes talking to other faculty as well as reading in other research areas. When "whatever it takes" fails to be a suitable choice, you must take a different approach.

Alternatively, suppose that you decide or determine that you will do the PhD in AI with only administrative support and no research support from a faculty advisor.

You should do whatever it takes to complete the appropriate research on your own. This includes reading journals and other dissertations in the field and communicating directly with other researchers in the field. This can/should include bringing faculty on to your committee who have the appropriate background in your chosen topic.

Suppose that you decide or determine that you must do your PhD in a certain topic (AI or something else) but for whatever reason cannot do it where you currently are.

You should do whatever it takes to find a new university + advisor + project that is of suitable interest to you. This includes researching where your topic is currently being done at other universities and contacting the faculty at that university directly to ask whether they have a potential opening for you to join the group. When "whatever it takes" fails to be a suitable choice, you must take a different approach.

At this point, you may likely realize that you will not be doing a PhD.

I would also consider if you can just swing the whole thing on your own.  You should realize that many professors (even in areas where they share expertise) append their names routinely to papers where all the ideas, work, and even writing came from the grad student.
At the end of the day, you sort of sink or swim on your own.  Now, it would be nice to have an expert in your field and some apprenticeship.  But academia is drastically far from an apprenticeship model.
If you think you can handle it, just do it all on your own.  Might even be easier since the old ma...person won't try to interfere with you.  Just add his name to your papers and plunge ahead as a stealth PI!
Honestly even when the fellow is in your field, you may find more collegial interactions with other professors, older students, postdocs, people from adjacent fields and conferences, etc.

Finding a co-supervisor is a responsibility of your primary supervisor. You should be guided on how to find a qualified mentor. Have you tried your own university? Can you ask help from your EE supervisor? What about asking your school to help (maybe someone else in your school has the connection)?
Sounds like you've just started the degree. If your supervisor and your school are unable to find a mentor for you, please consider suspend your PhD. No point for you doing an AI PhD unless you have someone mentoring you.

My supervisor asked other Professors from the same uni to join but they were all busy.

@asmgx are you sure you want to do this phd?

now that you are making it sound like a big trouble, I started to have doubts that I can continue. but I really really want to do the PhD

@asmgx you can still do it, just much  harder when you have no AI supervisor

Finding a co-supervisor is a shared responsibility.

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Already in grad school, but is grad school for me?
I'm sure you've seen a dozen variations on this question in this forum, but here's mine:
I've just finished my first year in a top US PhD program in the physical sciences, which I never dreamed I would be admitted to (ok, that's not true; I dreamed about it quite a lot!). It's gone all right, I guess--I wrote some code for my lab, got As in classes (although grades are utterly beside the point). But I'm not sure it's working out, or that I can make it work out. I'm not sure I have what it takes.
I mean, I can hold my own in technical conversations with senior coworkers--even (tactfully) point out flaws in their methods, suggest new approaches, pitch new experiments, and things like that. I've often heard "yeah, good idea!", only for nothing to ever come of it. What's that about? Are they being merely polite? Or am I supposed to be more "enterprising," somehow (whatever that means)? Is it my personality? I'm sometimes told that I need to be more aggressive or self-assured or something like that, but I don't really know what that means (other than "act like more of a jerk"), and don't feel comfortable trying to artificially change my personality in that way. What's wrong with saying "sorry?"
It seems like I'm missing something fundamental, maybe socially. It's been extraordinarily hard for me to get into the flow and be part of the team. Some other students walk into a room where people are discussing a project, make some goofy offhand comment, and get a coauthorship. On the other hand, I'm somehow having a hard time even figuring out who I report to, or if I have a me-project, or how to answer my questions about things like reporting and project ownership. There is something going on that I'm deaf to. I don't get it. Students at $PRESTIGIOUS_INSTITUTION aren't supposed to have this problem.
I also haven't really made any friends here. I'm on the shy side, but this isn't like me. I never knew I was so socially inept. If I'd been able to make friends, I wouldn't be bothering you kind people.
On top of it all, I'm no longer very sure that I believe in my subfield. It's not what I want to think about anymore when I go home. But I'm too old now to throw away the last couple years and start over.
I feel like I've wasted my year. Like I'll never, ever, ever be able to get a thesis together. Is all this the "bad fit" that people talk about when it comes to picking a lab? Am I just incompetent? Do I like reading about science more than I like doing it? Who do I even talk to about this? What do I say, and how do I get answers? Am I just completely trounced? Why don't I go take a job in industry and >quadruple my salary?
Maybe it's as simple as "you're fine; this is normal. Get some sleep and go join a club to find friends. Then you'll get less unhappy, and your work situation will improve." I just don't know.
I know there are no clear answers here, but maybe others have had similar experiences. Sorry if this is the wrong place to ask this kind of question, or if there are rules around asking imprecise questions or ones that are similar to previous ones. I also know that it's borderline impossible to infer my actual experiences or mental state from this kind of limited view, so there's no easy way for you to judge what's really going on. But I thought I'd try. Thanks.

What's your question? You write I know there are no clear answers here... which makes me think this question is off topic for this site. Also, did you look at the imposter syndrome  question on this site?

Also, browse the questions tagged "emotional-responses".

On the other hand, I'm somehow having a hard time even figuring out who I report to, or if I have a me-project, or how to answer my questions about things like reporting and project ownership.

This seems like the real problem. If you don't have a well-defined goal, it's natural that you would feel like you're floundering. Advisors are busy; if you don't tell them you need guidance, they will assume everything is working out (until they realize that it isn't, at which point they'll blame you). So, I would recommend asking for a meeting with your advisor (or whoever is in charge of your lab) and try to better define your research goals.
I think everything else you describe comes from this. Once you have a good project and are getting interesting results, you will feel more like a part of the lab and some of the other angst will disappear. Your lab might never be a perfect match for you, but I suspect it will suffice in terms of giving you a PhD and a chance to figure out what's important to you when you choose your next position.

Maybe it's as simple as "you're fine; this is normal. Get some sleep and go join a club to find friends. Then you'll get less unhappy, and your work situation will improve." I just don't know.

Pretty much. I would be aggressive in terms of finding a tractable project as described above. But I suspect the lack of sleep and friends are exaggerating the issue, and this is indeed very normal (as you say, there are dozens of posts here about it).

On top of it all, I'm no longer very sure that I believe in my subfield. It's not what I want to think about anymore when I go home.

This is perhaps the secondary problem -- after you get into a rhythm in your current lab/subfield, you may be able to pivot your projects towards areas you are more interested in.

Thanks for your feedback. I agree that this is probably the central problem, but unfortunately that doesn't make it much clearer how to proceed. I have managed to work up the courage to have that conversation with various postdocs, but it's one of those situations where everyone agrees "yes, must find a project. Sounds great," and everyone nods and smiles, but nothing happens. It looks like it would if they didn't want me there... But they insist that they do. I have to conclude that I'm not doing something I should be, but don't know what. Anyway, thanks again.

that doesn't make it much clearer how to proceed — Let me say it more directly: Ask your advisor for guidance. Specifically, ask your advisor to give you (or better: to help you develop) specific, concrete research projects/goals.

Oh geez, sorry if I seem like I'm being dense or something. Of course, you're right. I'm sorry. I'm such an idiot for bothering everybody when the answer is so obvious. I'm just chronically diffident and have a hard time doing it in practice. Sorry again.

"I'm sure you've seen a dozen variations on this question in this forum, but here's mine:"

Agreed.  Hope you have read the others.  But no worries on doing yours.

"I never dreamed I would be admitted to"

Little bit of a problem.  While it is true that there is an inverse Dunning Krueger impact, you also need to consider that there are way too many Ph.D.s created and a tournament system (google it) exists in academia.  So to be par, you actually need to be outstanding.

"I got As in classes (although grades are utterly beside the point)."

Don't completely write off grad school grades.  I heard the same thing (nobody cares about grades), but getting a 4.0 helped my get into McKinsey, GE, etc.  The strange thing is that there's almost an inverse of the inverse where you go from grades mattering to not, to to.  A contrarian to the contrarian.

"I mean, I can hold my own in technical conversations with senior coworkers--even (tactfully) point out flaws in their methods, suggest new approaches, pitch new experiments, and things like that. I've often heard "yeah, good idea!", only for nothing to ever come of it. What's that about? Are they being merely polite? Or am I supposed to be more "enterprising," somehow (whatever that means)? Is it my personality? I'm sometimes told that I need to be more aggressive or self-assured or something like that, but I don't really know what that means (other than "act like more of a jerk"), and don't feel comfortable trying to artificially change my personality in that way. What's wrong with saying "sorry?""

Academia, R&D, physical science is all about pulling your own sled.  That's great, that you are a smart cookie and give people good feedback.  But irrelevant and wrong intuition to cite it.  You need to do your own work AND GET PAPERS DONE.  That's the scorecard.

"It seems like I'm missing something fundamental, maybe socially. It's been extraordinarily hard for me to get into the flow and be part of the team. Some other students walk into a room where people are discussing a project, make some goofy offhand comment, and get a coauthorship. On the other hand, I'm somehow having a hard time even figuring out who I report to, or if I have a me-project, or how to answer my questions about things like reporting and project ownership. There is something going on that I'm deaf to. I don't get it. Students at $PRESTIGIOUS_INSTITUTION aren't supposed to have this problem."

Do your own stuff.  If needed, move to a field where it is easier to do independent work (not reliant on Space Shuttle flights or accelerator time...benchtop stuff).  Work world will be even harder in terms of team versus self and getting credit an all that.

"I also haven't really made any friends here. I'm on the shy side, but this isn't like me. I never knew I was so socially inept. If I'd been able to make friends, I wouldn't be bothering you kind people."

It sounds cruel, but you need to be more self directed.  Head down, butt up over the lab bench.  Get a girl/boy friend for social feedback (feel free to look outside your department).  Could you imagine a married person worrying so much about the social support?

"On top of it all, I'm no longer very sure that I believe in my subfield. It's not what I want to think about anymore when I go home. But I'm too old now to throw away the last couple years and start over."

This is a real issue. Maybe getting a computer programming job or the like would be better.  What worries me is you're not even suggesting a move to an adjacent or alternate field.

"I feel like I've wasted my year. Like I'll never, ever, ever be able to get a thesis together."

Anyone can get a thesis together.  There are a gazillion, forgettable doctorates year after year.  Really, this bar is low.

Net, net: I think you are a smart but young type.  Who probably did a Ph.D. because it forestalled a decision on what to do, and on getting a real job. (Bad call.)  You'd probably be better off either bailing or just buckling down and getting the "union card" in minimum time...and then moving to industry.  Kind of your call on how close you are.  If less than 2 years, push it through.  If more, bail.
I know this will come across as a non Stack Exchange answer.  But remember, you see your question as unique.   So, look at completely different ways of addressing your situation as well.

Some of these assumptions seem really unfounded....OP expressed doubt about his subfield so he should become a computer programmer? In particular, it seems very unfair to assume that OP started working toward a PhD to avoid getting a "real job." "To be par, you actually need to be outstanding" is probably true for future professors within that subfield, but OP's "union card" might allow them to find a different position at which they excel even if their doctorate is "forgettable."

Thanks for the feedback. I agree with cag51 that some of these points are making pretty big assumptions (how do you know I haven't talked with my S.O. about moving into a different field?), but there's no point dispelling them--and I do appreciate your perspective. To make an assumption of my own, it sounds like you're really the hard-driving no-nonsense businessy type. In defiance of all my intuitions, it increasingly seems to me like this kind of attitude is net positive in everything, including academia. How do you develop it? Do you have to have played sports as a kid, like they all said?

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text:
Gamification for theses and papers
It’s always been hard for me to stay focused and motivated when writing any kind of a longer text, e.g. an article, paper or gasp thesis, even if I’m interested in the topic and enjoy the work. The Stack Exchange and Wikia gamification systems with points and badges being awarded work well for me. I tried to reward myself for certain research and writing accomplishments, but realized early on that I’m not good at defining milestones and choosing bounties. Therefore, I hope someone else has done it.
Is gamificated thesis writing a thing?
Are there integrated software solutions to support that?
(I don’t use MS Word, but I could easily see how a plugin would detect the first quotation or the 10000th word etc. there. It would be great if it worked with Pandoc/Markdown or LaTeX instead.)
Related questions that are missing the gamification aspect

How to make thesis-writing feel less tedious? – Writing up existing results, mentioning techniques like divide and conquer or pomodoro time
Motivation to write the dreaded dissertation – What to do if you’re not interested in or convinced by the subject and degree, external motivation
Optimizing a thesis writing workflow for Bachelor and Master theses – General advice on structure and workflow

My experience is just the opposite -- I love doing research and writing papers, but tracking my progress makes it feel more like a chore.  I feel like it also reduces my creativity.

Are there integrated software solutions to support that?

Beeminder integrates with Github or Bitbucket, so if you keep your Latex or Markdown in either of those, you can automatically gamify your writing without having to do any manual data entry.
Beeminder is a commitment tracking service that plots your progress towards your goals, like this:

(image source)
There's also negative reinforcement: if you consistently fail to meet your goal, you "fall off the yellow brick road" and you pay actual money. Beeminder is good at tracking your progress without punishing you for small temporal variations in productivity.
In this context, your goal would be to make a consistent habit of adding to or editing your thesis.
With git integration you can set goals like "N commits per week" and data points are added to your Beeminder graph automatically when you commit.
There's also integration with Draft, if you prefer to count words edited rather than commits.  I haven't used Draft myself, but it looks like you can use it to write in Markdown.

Negative reinforcement seems like the opposite to gamification, though... gamification relies a lot on POSITIVE reinforcements for all the tiny steps.

@Layna Not really, gamification can involve both positive and negative reinforcement. Beeminder has positive reinforcement, too, in its "yellow brick road" graphs (see e.g. this comment). The problem with games that have only positive reinforcement is that when you get busy with other things, you forget about the game you're playing to win imaginary internet badges. That's harder to do when you have $$$ on the line :)

@ff524, you have an absolutely awesome knack for finding references or tools that address even the most obscure questions. Take my +1.

I have been a heavy user of Beeminder for a lot of things for the past 3-4 years. OP said he/she does not use MS Word. But for folks using Word, here is a way I mentioned in Beeminder Forum to integrate automatic Word count from MS Word to Beeminder.

One of the most common suggestions to aspiring writers (and this advice applies to thesis writing as well), is to make it a habit to write every day, say for 20 or 30 minutes. This is sometimes called a "Seinfeld chain", and you can find many apps where you can keep track of your progress, by ticking off each day you in fact write. The goal is to not break the chain, i.e. write every day. A low tech solution would be to mark days on a calendar. Some of the software tools allow you to see statistics, give you awards for completing a chain of a certain length, etc.
Another way to keep track of progress is to use something like Github, where you can also see a graphical representation of activity (in that case, number of commits).

Just to add, the Pomodoro Technique is based on 25 minute blocks, and the famous/infamous Write Your Dissertation in Fifteen Minutes a Day and of course the less helpful PhD comic which only requires 10 minutes a day.

Yes, the exact number doesn't really matter too much, as long as it is not too short (so you can actually get something done), or too long (gives a larger psychological barrier to even start the task, more difficult to plan).

On a playful note, you can change your daily task to: see a kitten every day! http://writtenkitten.co/

write every day, say for 20 or 30 minutes: That'll need to be significantly higher to write a thesis ;-) At least, for me it was.

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Mentioning other people's error in talk
I am a PhD student of physical chemistry, and my work involves developing packages for numerical simulation. Recently I tried to reproduce some work done with a rather well known package made by a (friendly) competing group, and failed. After some time, I found out that the discrepancy was due to an error on their part.
I asked my supervisor, and she suggested that I should write the authors. The corresponding author said the result was due to Author 2, a rather well known and respected senior of my field, and that he would get in touch. It has now been over a month, and I have heard nothing. I am still convinced that their result is wrong. I am hesitant to write back, as I don't want to appear nagging.
Here comes the tricky part. I have been invited to give a talk at a conference in June. The conference is a rather high profile thing in the US, and lots a possible future employers will be there to hear the talk. And, as I have seen from the participant list, also Author 2.
It will be strange if I don't mention that his software package is capable of doing the same calculation as I do, as part of my invitation involves giving a small review of the sub-field.
But if I do, I will have to say that they do it wrong.
I don't want my talk to become argumentative about a mis-calculation, as I want to make a good impression on a lot of people.
How do I gracefully talk about our work, without this turning into something ugly?

Perhaps a little patience is advisable(1-2 months). Regarding to the numerical package, I think you can send a comment to the paper they wrote if they don't give an eratum, assuming the error is not significant.

Does it make sense in your context to mention the bug without mentioning the name of the package explicitly? For example, by saying "A well-known package has bug XYZ..." .

mdiener: I think it would be percieved as snarky more than anything else :). Everyone in the room would know which package I was talking about.
Mikey: The jounal does not allow comments.

First, make double sure that the error is on their part, not your part, or there is not another reason for the discrepancy (e.g., they're computing something slightly different, they're using different assumptions, there's a simple way to transform their answer into yours, etc).
It seems reasonable sometime between now and then, to contact the authors (1 and 2), to check in and say you want to mention their work in your talk but you're concerned about this possible error.
Try to talk to Author 2 at the conference before your talk about this issue.
What to actually say depends on the situation and exactly how related their work is to yours, but if you are not getting any responses from them, you might just mention their work in passing.  You could avoid saying anything definitive about their work.  You can also just say that there seem to be discrepancies in some cases and you're looking into it.  I would avoid directly saying their work has errors unless they specifically admit that it does (in which case you still should not harp on it).

This is approach sounds tactful and also makes maximal attempt at ensuring that, if and when you do mention the error, it's not with malicious intent.

Thank you! That sounds like a very sensible and calm way of dealing with it. I am quite sure about point 1 already though. This is by now probably the calculation I have checked and double checked the most in my life - things would be way easier if I was just wrong :-).

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In academic papers, is it really a bad thing to not report the relatively not good results?
I am going to submit a computer science paper to a conference. So the thing is fairly simple. On some datasets with 1:1 ratio, our model achieves really good performance. On datasets with 1:5 or 1:10 ratio, our models only reaches mediocre results.
I just want to report the results of datasets with 1:1 ratio in this case since they are pretty good. Putting a mediocre result (on the 1:5 or 1:10 ratio datasets) may extremely lead to a whole paper getting rejected. However, my PhD advisor told me that we have to put the mediocre result as well.
Meanwhile, the PhD advisor seems really angry about this behavior and disappointed about me, and I feel really sad because I didn't cheat, and I just not want to include the relatively mediocre results in my paper.
I think many other computer science papers do something like that, not reporting their results on some datasets if the result is bad, and reporting their results on some datasets if their result is good.
I don't get why my PhD advisor is so angry about that. Could someone tell me? I am really appreciated it.
Currently, I feel wrongly judged by my PhD advisor and afraid of the whole paper getting rejected because of this since we are going to submit to a top-tier conference.

What is your objective, trying to win support for your model by presenting selective information or giving an informative and useful analysis of the model? To be more blunt, is the paper intended to be an advertisement for your model or is the paper intended to be a scientific analysis of the model?

You can put that in the future work section so it sounds less negative. It's not an oversight, it's something to improve. Applying ML on imbalanced datasets are a common problem that many people have tried to solve, so reviewers may not be that surprised with your findings.

If you omit the poor performance data, what do you expect will happen when another researcher reads your paper and discovers the method doesn't work on 1:5 or 1:10 ratio data sets? They will publish another paper saying that your method doesn't work. Or they will publish an improvement to your method that does work, and your paper is then a useless bit of history.

Finally a supervisor is mentioned on SE who does not try to 'beautify' the data. Finally a supervisor who has an upright ethical standard. OP should be happy they have a supervisor with integrity and honesty. Learn from them, these are unfortunately rarer than one would wish for! Apologize and accept what they say, they are an example for how a scientist should be. You must be absolutely trustworthy in your reporting and your supervisor needs now to be convinced that you are. You have to put the work in to gain their trust back.

Wouldn't what you are doing be pretty easy to see through? Isn't testing a result under many circumstances typically included in research? Especially since it's so easy to do in this case? It's not biology where observations can take months to collect.

Do you have a valid explanation why your model does not work well on other datasets? If yes, mention it. Science is about learning something new, not being the best. In my field (not CS) every single paper claim that they are the best, but how is it possible? I read plenty of papers, and I questioned why I was even skimming the given paper in 3/4 of the times! There was nothing new or instructive in the. In the worst case you will reach your paper in for a lower level conference. You will still have a paper. If your results are not something others can build on it won't be cited anyway...

Report your findings. It's ok if a model works in some situations and not others. That is expected. It will be helpful to future readers to know this so that they don't waste time trying to apply your model to a situation where it is already known to not work. Every famous model has limitations. Imagine if those limitations were kept secret! It's ok to make mistakes, this is what learning is!

I definitely agree with your supervisor that the full results should be reported: as suggested in comments & answers here, omitting mediocre results is bad science and borderline unethical. However, depending on how the interaction went, I would hope that I/your supervisor would treat this as a "teachable moment"; there's not necessarily any reason you should already know why this is such a bad idea. In your supervisor's position I might be quite forceful in expressing my opinion, but I hope I wouldn't actually be angry at a student unless I thought they there were trying to deceive ...

Does the metrics you use depends on the ratio of positive:negative ratio? E.g metrics like MCC, accuracy, F1 score, etc. DOES depend on the ratio. Does your specificity/sensitivity (and similar metrics which does NOT depend on the ratio) also change?  Are the datasets coming from different sources, e.g different types of experiments with different reliability?

What is the meaning of 1:1, 1:5, 1:10?

I this that the title should be changed to something less general than academic papers as a whole. The answer can be yes or not depending on how the paper is tailored, discussed, and the nature of the possibly omitted results. Bad results are not results,in general. Writing in a fashion that can mislead the readers should nevertheless be avoided.

@CaptainEmacs. Amen.

Consider this question posed by a PhD student who found belatedly that their work was based on a fraudulent paper, and spent time fruitlessly trying to replicate its claims: "I am still quite angry about this issue as this has cost me about a year and a half of my PhD": https://academia.stackexchange.com/questions/166813/previous-publication-of-my-supervisor-and-a-post-doc-is-a-lie

I heard that this thinking was such a problem that some publishers require proposals to be submitted before the experiment is carried out so that researchers are forced to publish the mediocre results. That is how critical this information is. You won't be rejected because if it

“I have not failed. I've just found 10,000 ways that won't work.”
That's 10,000 ways of scientific progress

In addition to everything else mentioned, I recommend the book "Science Fictions" by Stuart Ritchie.

Circumstances alter cases. How "relatively not good" were the results?

Others have conveyed extremely well that you absolutely must honestly report how your model works to the best of your current knowledge, though (a) there are probably ways to improve the model on imbalanced data sets and (b) you can emphasize the positive results in your talk/paper. I just want to add though, that I don't think this situation is unfixable. You do need to learn from this and change your behavior, but it's also a learning experience where you can become a better scientist. There are supervisors who would pressure you to cover up the bad results; your advisor is a very good one.

Research is a search for truth, not for happy talk. If you have evidence of some behavior then you should be honest and report it just as it is. Don't try to dress it up.
Giving only the "best" results will sometimes mislead people and will cause them, perhaps, to waste time and effort.
You are currently the best source to give an honest and complete review of the meaning of your results. I strongly suggest you do that. At some level, not doing that is malfeasance.
I suggest that the reviewers themselves probably think like I do and won't reject a paper that is honest and complete, other things being equal. They aren't looking for happy talk either.
But you may also have an opportunity to investigate why you get different outcomes. Is it something about your model, or something special about the data? Inquiring minds want to know.

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This is indeed bad. It is worse than it appears from the title of the question.
Your situation is that the model performs well on a very balanced dataset and ceases to do well as soon as imbalance creeps into the data. This seems to be a preliminary model, because there are so many ways to address the imbalance now. You must implement those and then see how the model works.
Presenting only the results on 1:1 data alone conveys the false picture that the model works well universally. Keep in mind that ML/DL are used very commonly now in different fields, and someone not familiar with the nuances of data balancing may pick up your work and start using it directly. I work in a non-CS area that is starting to adopt ML, and poor models can have very bad real-world impact.
I hope you now understand why the advisor is unhappy. In your situation, I would apologise and consider not submitting if I don't get actual robust results (considering that its a top-tier conference you are looking at).

Apologize for what?

@yarchik - For attempting to selectively report results. At best its careless, so no big deal, it is a learning opportunity.

It's bad because large scale behaviour like this pollutes science literature with bogus results, even if researchers are well intentioned.
It's a large scale problem, specially (but not exclusively) in health sciences, where ineffective treatments can actually kill people. See the Chloroquine and Ivermectin fiasco in the current pandemics.
Even completely "negative" results are valuable, because they put the results of other researchers in perspective, and avoid people getting into dead ends. Unfortunately its still hard to publish "negative" results, and it hurts academia and generates perverse incentives.
The must read article regarding this is Why Most Published Research Findings Are False(1), by John Ioannidis:

Summary: There is increasing concern that most current published
research findings are false. The probability that a research claim is
true may depend on study power and bias, the number of other studies
on the same question, and, importantly, the ratio of true to no
relationships among the relationships probed in each scientific field.
In this framework, a research finding is less likely to be true when
the studies conducted in a field are smaller; when effect sizes are
smaller; when there is a greater number and lesser preselection of
tested relationships; where there is greater flexibility in designs,
definitions, outcomes, and analytical modes; when there is greater
financial and other interest and prejudice; and when more teams are
involved in a scientific field in chase of statistical significance.
Simulations show that for most study designs and settings, it is more
likely for a research claim to be false than true. Moreover, for many
current scientific fields, claimed research findings may often be
simply accurate measures of the prevailing bias. In this essay, I
discuss the implications of these problems for the conduct and
interpretation of research.

There is a XKCD comic that showcases it nicely:

(1) Ioannidis, John P. A. “Why Most Published Research Findings Are False”. PLOS Medicine, vol. 2, no 8, agosto de 2005, p. e124. PLoS Journals, doi:10.1371/journal.pmed.0020124.

+1 especially for "Even completely "negative" results are valuable, because they put the results of other researchers in perspective".

The Ioannidis paper is great, but I don't think the xkcd comic is exactly the same thing

@AzorAhai-him- Surely, but it showcases one of the problems raised by Ioannidis, how the bias toward only publishing positive results lead people to believe in bogus findings.

Loving the xkcd item, actually. Should be "required reading" for every doctoral student doing statistical work.

I think the OP's comment that including "mediocre result may lead to [the] whole paper getting rejected" sums up the situation very well. It's basically saying, "but if people knew the whole truth about my work, they wouldn't care!"

Good answer but Ivermectin may not be the best example. 42 out of 42 studies showed mortality improvement. Despite the fact that many of those studies had issues, we should be funding large well designed studies using safe doses. See ivmeta, see however meta analysis preprint; especially the comments there.

The 'mediocre' results are good too. They tell you under what circumstances you get good performance, so people know that if the data has a 1:1 ratio they can use your model, and if it doesn't then they should do something else. That's helpful - it improves overall performance by limiting use to where it is most useful.
Knowing what leads to good/bad performance also gives information about why the model works as it does, and can lead to insights into how to improve it. If all the results are good (or they're all bad), that doesn't tell you anything about how to improve it. The most informative result is one that shows both.
The purpose of a conference is not to boast about your achievements, but to convey useful information to other researchers. So the more informative you are about what works, what doesn't, and what to look at to improve things next, the better it is for the conference's purposes. Imagine you are explaining your work to somebody thinking about working on it themselves - what would be most useful for them to know? How can you help them? That's what you should be putting in your paper. You are giving them a map showing the areas you have explored - how pleased would they be with you if you only marked in the treasure and left off all the booby traps and pitfalls?
There are two definitions of 'good' here. You are thinking only of whether the model is good - how accurate it is. But you also need to consider whether the research is good, and the paper. This is about how much detail the paper gives about the performance, about when and why it performs well or badly. A paper that reports in detail exactly why a particular type of model doesn't work may be considered excellent from a scientific point of view, and tremendously useful to other researchers. The science can be good, even if the model is bad. Your priority should be to do good science. Producing models with good performance is useful too, but secondary.

In many cases, readers would only need to know one thing about many of the negative results: how many there were.  If one formulates dozens of hypotheses until one is found that seems to hold up with 98% confidence, knowing how many hypotheses were tested would provide a reader with some clue about whether that 98% result is likely to be significant or spurious, or whether more research is warranted.

There are many answers who address the underlying issues of why such a paper needs publishing all the relevant results and I agree with most of them. I'll simply add a blunt and clear statement of why as a supervisor I would be disheartened or angry with you:
A PhD is not about publishing x papers in the champions league of journals, a PhD is about learning how to be a good scientist!
Hiding results for personal fame (publishing in top tier journals) by giving the impression of an awesome breakthrough when you know it is not the case, is an absolute counter-indication of a good scientist.
This attitude leads to the dark side. Next you will omit some "risky" tests right from the start, because you assume they will look bad, select data that you know your algorithm will work good on in the first place or outright doctor it to fit, because, well,you want to show the good results, so why not prepare a test case that fits the strengths of the algorithm, right?! Who cares about the rest when the paper gets accepted.*
From a good supervisor's point of view they see that they failed in teaching you so far and try to correct that now pretty late.
If someone published 10 papers at top journals, but omitted data, and another one did average work on average projects, but following proper scientific standards of full transparent disclosure, I'd take the second one any day over the first when I look for a good scientist. The first one is a self-seller, but they haven't earned their PhD. I'd hire them perhaps for the marketing department, if I'd work at a company that doesn't care about how they sell their stuff from an ethical point of view.
Personally I'd have felt ashamed and would have feared to be expelled had I ever brought up the idea to "just omit" bad results to my PhD supervisor. You don't necessarily need to focus on them, if you want to argue for whatever use-case the test scenario is not so relevant, but you definitely need to report them. I.e. interpretation can have focus, data presentation needs to be complete.
* There is a difference between 1) selecting data sets that match the  problem scenario the algorithm tries to solve best and indicating no effort was spent yet on going for other data sets either in the same category (where results are unknown so far) or another category where one might state it could be interesting (potentially perform worse) because the algorithm doesn't fit that data and 2) leaving out data sets because one knows of quirks in the algorithm that make worse performance likely but not mentioning it, 3) already having results for data sets that fit well but omitting them because the algorithm behaves bad on them without even mentioning it and 4) other scenarios I didn't include because they weren't in my mind at the time and it wasn't the purpose of this answer to give a full list (those scenarios might be either okay or not).

Doesn't whether the advisor is appropriately angry depend on whether the student has had a chance to learn about this aspect of scientific ethics? Really depends on whether this issue has been clearly communicated to the student previously ...

@BenBolker "why as a supervisor I would be disheartened or angry" "From a good supervisors point of view they see that they failed in teaching you so far and try to correct that now pretty late" should imply that the exact emotion depends on the context (and still might be irrational) and that not only OP would be to "blame", which doesn't change the why. Though it is also disheartening by itself if that is something that is not inherently clear at least on some level, but indeed, then it should be taught ideally.

Failure to report the mediocre results is unethical
When writing an academic paper, pretending that you did not get bad results for some cases that you were hoping to get good results from is unethical and damaging, both to your credibility/reputation, and to the field, as others may waste time trying to use your code in cases that it doesn't work for.
But take note...
There's a difference between reporting your mediocre results, and emphasising them. Your method works well when the datasets have a 1:1 ratio. This should be the central focus of your paper. Focus the paper on that aspect - your new method is great for those cases!
The mediocre results are simply other results. You have shown that it works well for 1:1 ratios. You have also found that it does not do well for 1:5 ratios. This is also useful information. Make a note of it - you have examined the performance of your approach for cases with 1:5 ratios, and found that it does not perform as well.
Not only is this a way to be ethical while also focusing on the good, but it gives you the opportunity to point towards further research possibilities - why doesn't your method work for 1:5 ratios? This is clearly an area for further investigation! If you know what's causing the problem, then simply point to it and note that further work is needed to extend the approach to work for these cases.
It's to your benefit!
Your first instinct might be to think that people will dismiss your work because you mention some weaknesses. This would be folly. People will see your approach, think of a new way to adapt your approach to other cases, and publish... referencing YOUR paper as they do so. And that increases your citations.
If you don't mention the weaknesses, then it's less likely that people will spend time thinking about how to adapt your approach - instead, they'll dismiss your approach as faulty when it doesn't work for their situations, and ignore it.
Get the balance right
You need to mention the mediocre results, but they shouldn't be at all central to the paper. The trick to this is to frame the paper as focused on the 1:1 ratio cases. It is here that the model works well, and thus you are reporting a new approach to 1:1 ratio cases. When discussing the results of the model, you include a small section discussing what happens for 1:5 and 1:10 ratios - this should not be to the same level of detail as the 1:1 ratio cases, however!
"When applied to more unbalanced ratios, such as 1:5 ratio cases, it is found that the approach does not perform as well, achieving only XXX where other approaches can achieve YYY. This can be seen, for example, in case ZZZ,..."

Do the right thing and put all the results on the paper. It's part of ethics in research, and the work of a researcher is (or should be!) producing new knowledge. Obscuring your results, putting only what looks good is indeed a violation of the ethical code of the research.
On the other side, your supervisor should not be too mad at you. Part of doing research is failing...
If you have such bad results you should also consider submitting the paper to a less prestigious conference, you might get published even without outstanding outcomes.

I don't have anything intelligent of my own to add, but I think Richard Feynman pretty much addressed this:

“…the idea that we all hope you have learned in studying science in school — we never explicitly say what this is, but just hope that you catch on by all the examples of scientific investigation.  It is interesting, therefore, to bring it out now and speak of it explicitly.  It’s a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty — a kind of leaning over backwards.
For example, if you’re doing an experiment, you should report everything that you think might make it invalid — not only what you think is right about it: other causes that could possibly explain your results; and things you thought of that you’ve eliminated by some other experiment, and how they worked — to make sure the other fellow can tell they have been eliminated.
Details that could throw doubt on your interpretation must be given, if you know them.  You must do the best you can — if you know anything at all wrong, or possibly wrong — to explain it.  If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it.”

— Richard P. Feynman [Caltech commencement address, 1974, quoted in Surely You're Joking, Mr Feynman!: Adventures of a Curious Character] (my emphasis)
(He was of course talking about scientific theories, not data models; but since both are aiming to expand human knowledge, I think the same principle applies.)

Putting a mediocre result (on the 1:5 or 1:10 ratio datasets) may extremely lead to a whole paper getting rejected.

That is a secondary problem that is generally a result of bad refereeing or a bad attitude from the journal.  If you have developed a method that is good in some narrow area, but mediocre in another area, it still sounds like a useful method and it is still useful to hear about its performance across various tasks.  Good academic journals should generally be open to papers talking about "negative results" where we looked for some effect and didn't find it, or developed a new method but it didn't work well on some kind of problem.  This is useful information because it allows researchers who follow in your footsteps to know what has already been tried, even if it failed.  (Refusal to publish papers like this leads to a result that is somewhat similar to the injunction, "those who do not known history are condemned to repeat it".)

Well, if the question is why your supervisor is unhappy with you, I don't think we can help you, but in general, the situation is not that terrible at all. It is quite common, for example, that a result which is going to be published is impressive only within a specific region of parameters. The model is working here, and fails there and everywhere else, for example. Of course there is a temptation to hide those less impressive sides of the findings, and of course, sometimes people do this in publications (although they should not).
As a non-specialist in CS, I do not know how bad for your publication is what you said about your model, but your supervisor should know it. If they say the results are publishable, they probably are. If not, well, tough luck. The inclusion of mediocre results does not necessarily make your paper worse; a lot depends on the wording and subtle details. Usually, there is more than one way to publish a particular result and to build up a paper around it.  As a PhD student, it is totally fine if you are not sure how to present your results better, and this is where your supervisor should be able help you. A honest discussion between you and them (and possibly other co-authors, if any) on how to improve the paper is probably necessary at some point.

Another perspective: I see an XY problem.  OP asks if the not reporting the relatively not good results solution is bad.
One could also ask:

Should academia require that results be reported without bias?

or

How did and should academia change to reduce bias in reporting?

or

Some of my results are positive and others are negative.  What should I do?

Maybe a paper exploring the question of what to present and why would be better than a paper presenting some or all of the results.
It seems to still be the case or perception in much of academia, that surface-level-success in academia is still facilitated by not reporting the relatively not good results.
In that part of academic publishing that involves reporting human clinical trial* results, there is a push to require pre-registration and subsequent reporting of clinical endpoints, but this is not (!) enforced. In very important trials.  The solution doesn't seem to be working when it's most important that it does.
*(Not all clinical trials are in humans. Veterinary clinical trials are a thing.)

How can we make it so, in academic papers, is is not a bad thing to report the relatively not good results?

Now there's a topic worth researching!

It's possible to lie by omission too. You're talking about deliberately leaving out a key fact in order to make it appear that your method works better than it does. Even if everything you say is technically true, you'd still be deliberately deceiving the reviewers in order to manipulate them into signing off on a publication that you think that they wouldn't agree with if they knew the full truth about it.
From the Wikipedia article I linked to:

Lying by omission, also known as a continuing misrepresentation or quote mining, occurs when an important fact is left out in order to foster a misconception. Lying by omission includes the failure to correct pre-existing misconceptions. For example, when the seller of a car declares it has been serviced regularly, but does not mention that a fault was reported during the last service, the seller lies by omission. It may be compared to dissimulation. An omission is when a person tells most of the truth, but leaves out a few key facts that therefore, completely obscures the truth.

Besides, there's no expectation that every method will work equally well in every situation. The fact that an algorithm doesn't work well in certain circumstances is potentially useful information, too (especially if you can identify an interesting reason that that's the case).

I agree with the previous answers that it is unethical. However, even publishing a paper that hides 'mediocre' data can backfire: suppose someone replicates the process but performs those tests you omitted and find that the outcome is mediocre. That person might publish and your paper will be no longer be 'famous'.
Don't cheat to get fame because it won't get you any in the short or long run


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How to keep confidential my PhD position search, if I have already started a PhD in a lab, but want to change
I am currently hired as a PhD student in the lab for 8 months and I would like to look for another PhD position. The main reason is that my interests don't fit the research focus of my current lab. The project I was recruited for is a collaboration with another group (and I am much more interested in their field of research). However, the project seems not to work out. I have another project in parallel, which fits research focus of my current lab, but I am not that excited about it. My current PI is not an expert in the field I am excited about.
I understand now that it was sort of a strategic mistake from my side, to accept an offer from the PI, whose research interest does not correlate that much with mine. Therefore, I consider to apply for other PhD positions in the field, I am more interested in. And I have doubts, how to make the application process smooth.
I understand that it would be better to talk to my PI and explain him my concerns and why I want to change the lab. However, I am afraid he will not take it positively and I might loose current position before I find new one. So it might make sense to keep it confidential before I find a new position.
My question is: when applying for a new position, should I mention in the CV the name of the PI, in whose group I am working now? I know, I should not hide that I  already have a position, but would it be better to describe project and techniques I learnt so far, but without contact information of PI and name of the lab? Or should rather I give this information in CV and simply explain my reasons to change and ask in motivation letter to keep confidential that I want to change the lab?

First you might want to assume that you can't actually guarantee that it will be kept confidential. Senior people in a given field tend to know one another. If you make that assumption you will act in a way that it doesn't harm you if/when it comes out.
Many will, however, honor a reasonable request to not contact some other group if you have a reasonable explanation.
Additionally, most people, those who aren't abusive, can understand that some people need to move on so you may have unfounded fears about what will occur if (when) they do find out.
If you have a good relationship with your current PI then talk with them about your needs. Perhaps they will even give you a good recommendation. But if you have reason to fear their reaction, such as a poor work environment or retaliation against someone else, then put a note in your application that you would prefer that your current employer not be contacted initially. Later, if it looks like you might get hired in the new place, then they will probably need to make contact, of course, but you should be farther along in the process by then.
But it would probably be a mistake to hide the identity of your current institution/project. You don't need to name the supervisor, I think, but the project at least should be named. Otherwise, people might wonder if there is a problem that you aren't being straightforward about.

Thank you for your answer! Do I understand correctly that, in your opinion, if I want to keep my application confidential initially, I can mention in the CV the institution and name of the project, but not specify the exact lab and PI name? And in my cover letter I should explain my reasons to change and ask not to contact my current PI at the beginning, at least before they consider hiring me?

That sounds about right.


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How to apply with past postdoc or PhD positions which did not result in publications or recommendation letters due to conflict with supervisor?
To make the long story short, at the end of my PhD (Just 3 months before my viva), I realized that my PhD supervisor is liar and totally dishonest, probably he has his own reasons, but this is what I realized. I talked to the head of the department to quit my PhD and I was told it is better to finish it, as it is too late. He asked why I did not report the problem sooner and I replied that even I could not imagine this situation one year ago. My supervisor and I are from same country, but he is a professor at a European university.
I decided to talk to my supervisor, but the discussion was too strong and I told him he is not eligible to be a teacher (professor), because he is not an honest person; the most important criterion for being a teacher.
Any way, he did not support me to get a reasonable postdoctoral position and as a result, I ended up finding myself in one of the very well developed East Asian countries. The PI was a young scientist without proper experience (we were his first group members). He assigned me a computational project and when I asked him what we are looking for, he said just do what I have asked you, run the simulation and at the end you will realize. Repeatedly I talked to him and told him there is nothing special in this simulation and we should carefully know what we are looking for before pushing the enter key and running the simulation, but he insisted to running the simulation. I asked him to change the project, but he refused.
I have finished the extremely heavy simulation, which took 1 year, analyzed the data and extracted what I could extract from the simulation. I presented the results and it was clear that there was nothing interesting in the simulation. He said “you are postdoc and you should think about what you are going to do”. I was aware of the fact that in East Asian culture I should not criticize my PI, but I believe in science community we can and we must do that. Science means working with the facts and reality. After a couple of more wrong information and recommendations that he gave me, I eventually told him he was wrong. It triggered a personal war between us and he fired me – after two years of hard work without any publications.
I was lucky enough to a new position in Southern hemisphere. Now whenever I look for a new job, I am asked for 2–3 letters of recommendation and specially some of them asking for letter from my PhD supervisor.
How can I handle this situation? What should I say when I apply for a job? Should I mention the conflicts or does it make the situation worse? Should I say that I only have one letter of recommendation? What should I say about my low number of publications?
I should mention that at each stage I have changed my research field; my PhD and the postdocs all are different topics in physics.

I should mention that I have a few publications from my PhD and current potdoc position.

A! I am not answering the question but just want to say your experience is almost the same with mine (though I am just a 3rd year phd student). Maybe the best lesson I have learnt from the unpleasant experience is that getting along with any kinds of people is very important. This may not be an honest or moral lesson but it works all around the world.

@HanaKaze I read your post. You have lot of options to do, you can start small projects with other researchers which helps you to learn new things and probably some publications and letters of recommendation. Expand your scientific network and talk about your research, your ideas to all the researcher that you meet, regardless of their research field, it will help you to find job and it brings more new idea. There are some student exchange scheme where you can attend different universities for (I think) up to 1 year. Changing the field from physics to psychology is a big decision ...

@HanaKaze Personally, I believe everyone is interested in psychology because it is much much easier to understand than physics and it is closely related to our everyday life. You can do physics and you can find job after graduation provided that you do some research on projects which lead to the proper job, for example you can do some research on polymers and there is higher possibility to get industrial job. If you do a combination of theoretical-experimental project, you will increase your chance to get better job...

@HanaKaze In some cases mathematical modelling are good choices as there are many places who are looking for people with such expertise. If you do string theory, then the number of options are limited to mainly academic jobs. Finally, be creative and do not let anything put you down. You can make a bad project to a brilliant idea.

As you may realise, telling the story as outlined here to people asking for references may not be sufficient to convince them. If you know an area in physics where you will be applying for jobs, you can make contact with professors in this area and offer your help in some small project (2-4 weeks). Make sure you do a good job and have a good interaction with them. After you are finished, ask them if they could be your reference. If this area in physics is very close to the tasks in the job it should be a more relevant reference, and therefore it will not be too strange that they are your reference instead of your supervisor from the PhD.

Thank you for your recommendation, actually, I have tried to do research in collaboration with other researchers, but in many cases they did not agree. The research during my PhD was a theoretical work which is not popular at all and after that I was always new to the field so people could not see my outputs to start the collaboration. Now I have some publications, but my contract ends soon and I should find a new position. Finding fund for short term visit is tricky.


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What does it mean to be offered waived-fee OA publishing?
I submitted a manuscript to a journal for non-open-access publication and the editor came back and said that they want to publish the article as open-access (same journal) and waive the fee for this.
As not to be too broad, what implications does this have for my paper? Is it generally viewed positively to have OA publications? Really, in the end, I am trying to look at the pros/cons of saying yes/no to this.

"they want to publish the article as open-access (same journal) and waive the fee for this" -- maybe ask them why they want that for your article in particular?

This means that your paper can be more widely accessed. Personally, I see no cons! But maybe I'm missing something?

It is still an open question whether open access actually leads to more citations or not.  This is at least partly because the models and players in OA are still rapidly shifting, especially with the rapidly increasing influx of low quality or scam publishers who have embraced open access because it is an easier way to bilk money out of insecure early-career academics.
I will assume, however, that you have already vetted the journal and know that it is a significant and reputable one in your field.  Let us also assume that it really is just as you present: the article will still be published in exactly the same way, just not behind a paywall and this will cost you nothing.  In that case, the general wisdom is that, all else being equal, an article that's easier to find and read is likely to have higher impact than one hidden behind a paywall.
Free open access in a normally closed journal is actually less unusual than you might think.  For example:

Nature makes certain types of large community studies open by default.
Special issues and special collections in a journal are sometimes negotiated to be open access by their organizers.  If your article fits with such an upcoming collection, it would be natural for it to become included in the policy.
Certain types of information, particularly in biomedical research, are required to be published open access (either immediately or within certain timeframes) by some government funding agencies

The real question is: what is the reason in your particular case?  Ask the editor. If it's something that makes sense, like one of the cases above, then great!  Embrace it and get some extra citations.  If not, then ask yourself again whether you really trust this journal...


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What is the etiquette for using empty desks in a communal grad student office?
I will soon be visiting a friend who is a graduate student.  She works in a communal office with around 50 other desks (which are mostly occupied by other grad students).  Is it appropriate for me to study at an empty desk?

Have you considered asking them?

Think of it like visiting somebody else's house.  Your friend is your host, but also shares her "house" with a bunch of roommates.  She is the one who will know best what sort of etiquette is followed in her community.
Why not ask her, "Where would be a good place for me to study around here?"  You might get sent to one of the desks, or to an empty meeting room, or the student lounge, or the coffee shop down the street.  There's no way to know without knowing the lab culture, and your friend is the one in the best position to judge.


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Name of the explanatory chapter in a cumulative dissertation
How do you usually name the chapter of a cumulative dissertation (PhD thesis) that describes the research? A cumulative dissertation basically has two part: One part includes (published) research papers and the other part is an explanatory chapter that motivates the research and summarizes the it.
So basically I thought of the following:
'Background chapter'
'Frame chapter' (that would be the direct translation of the German term
I'd apprciate every comment. Thanks in advance.
Thanks for the current answer. Are there any other suggestions. I'd be happy to hear any comment on that.

If your thesis advisor and you settled for a cumulative dissertation, s/he may have examples by previous PhD candidates in the shelf which meet the standards by your local department.  Or offers a suggestion, like e.g., theory if the papers share a significant portion of background.

Some places, such as Heidelberg, simply call it a "Summary". Others indicate that the Summary must place the papers in their "Scientific Context". I would favor the latter. But a specific university might provide specific guidance on this, as might an advisor.


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How to send letters of reference (LOR) online?
I am applying to university very soon and my LORs are already ready. I will send my application online. But how do I send LORs? Do I have to get my recommenders sign the letter and scan them and add it to my application? Or do I need to write a LOR in an electronic version with signuture pasted? I would be very glad if anyone answeers back.
Thanks)

What are the instructions from the universities you are applying to?

Does this answer your question? Do I have to sign a recommendation letter that will be submitted online?

Also: Scanned Letter of Recommendation versus Saved as PDF

Usually your letter writer will send the letter separately so that you can't read it. They will be able to follow the instructions provided by the university.

Assuming confidentiality here, generally the application system will send a link to a form where recommenders can upload their letters (without you having to see them).  Or in some cases they will have to e-mail their letters to a person on the admissions committee.