text:
Addressing the Impact of Unknown Authorship on PhD. Students’ Academic Reputation and Remedies
As a current PhD. student, I have observed some troubling practices. It is not uncommon for advisors to add students’ names as co-authors on papers resulting from collaborations with other research groups, often without the students’ prior knowledge. Students typically discover their authorship only through notification emails, frequently after publication, and have often never seen the paper before. This situation is both frustrating and disempowering, especially since these papers are usually unrelated to the students' PhD. dissertations. I am concerned about how such involuntary authorship might affect students’ academic reputations. Could this impact their PhD. degree? What measures can prevent such issues, and what steps can students take to address and mitigate the repercussions of these ‘forced authorship’ after publication
Additional info
Some advisors take pride in these practices because having their students' names appear in numerous publications can, to some extent, enhance the students' academic visibility. Advisors may assign authorship based on their preferences, often listing names of students they favor—those they deem promising—in many collaborative papers with other research groups, while other students may not receive such recognition.
Typically, students whose names appear on these papers might have contributed by sending samples or have specific experience related to certain experiments or simulations detailed in the paper. It is indeed perplexing that authorship can be assigned for minimal or indirect contributions, or even for work that is only tangentially related.
Students are aware that these practices are unethical. They have privately confided their concerns to me, and I fully empathize with their distress. They often have no knowledge of how these decisions are made, and by the time they discover their names appear in a publication, the journal has already received the submission.
Directly questioning their advisor is not feasible because the issue has already occurred. After publication, raising concerns becomes even more difficult. Publicizing the issue could result in the retraction of the paper, which would be a severe setback for both the advisor and the students, potentially jeopardizing the students' research careers. Sometimes, after publication, if the advisor remembers, they might provide a brief explanation to the students; if the advisor forgets, no clarification is given.
Consequently, many students remain silent, with no evidence beyond the journal's email to demonstrate their lack of involvement.
Thank you for all the feedback.
I would like to provide some further clarification. While the practice of advisors adding students' names to publications without their involvement is not widespread, it remains a “tradition” in certain research groups.
Our field is centered on the design of electronic devices, with a particular focus on sensor design. In this context, advisors often engage in extensive collaborations with other research groups, leading to the publication of joint papers. However, the students under these advisors frequently have no direct involvement in these collaborative projects, even if their research areas are somewhat related. For example, consider a collaboration on the design of a temperature sensor. A student with substantial expertise in thermal simulations—similar to those required in the project—may have their name added to the publication, despite the fact that their simulations involve entirely different parameters. The student may only become aware of this when they receive a notification from the journal after the paper has been submitted. This is just one possible scenario. Typically, the student’s name appears far down the list of authors, usually beyond the fifth author. The justification for including the student’s name merely due to a general research area overlap is, at best, questionable.
I am seeking guidance on whether there are any institutional mechanisms that could prevent such situations. Additionally, what steps can students take to protect themselves from potential accusations of academic misconduct? Naturally, contacting the editor to request a retraction seems too extreme. Furthermore, considering that these papers are usually unrelated to the student's dissertation work, could these practices have any impact on the dissertation?

In what country/countries are you seeing this? Are the papers being published in reputable journals?

Did the student's contribute and what fields? For example, labor intense projects from the lab- or field-based projects might include the student making a meaningful contribution even without helping write. However, it is generally considered unethical to submit a paper without letting all coauthors read the manuscript.

To my knowledge, this issue is prevalent among research groups in many East Asian countries, although these journals are typically lesser-known and of lower profile.

@user_ababa_gwzddx can you edit those details into your answer? Also, welcome to the site. I would encourage you to take the tour.

@Richard Erickson  It is not as labor-intensive as fields like biology, nor does it depend heavily on individual expertise as in mathematics or physics. Instead, it pertains to areas such as sensor design and flexible electronics. Typically, students' research areas have some relevance to the papers they are 'forced to be listed as authors' on, even though they did not directly contribute to the work. The rationale of attributing authorship based on the relevance of research topics is quite puzzling.

@Richard Erickson  Thank you very much for your prompt reply！

@Richard Erickson  This issue also reflects certain flaws in the submission process. Some journals require all authors to provide signatures upon submission, confirming that they have reviewed the manuscript and are aware of their authorship. However, many journals rely solely on the corresponding author’s assurance that all authors have consented to the publication, which creates a loophole. It is this loophole that enables advisors to arbitrarily assign authorship to students.

Unwilling is different from unknowing, and usually worse. You describe unknowing authorship here.

@JonCuster  To be precise, the situation is as follows: before submission, the student is unaware; after submission, even if unwilling, the student must reluctantly accept the reality.

Most journals send a confirmation email at submission where those who object can contact the editor, failure to do so means they agree with the submission. Stating that the student must reluctantly accept is also nonsense. You mention "disempowering" in your comment. All authors have the power to disapprove of a publication throughout the process, however, with that a situation can become awkward, uncomfortable, and have consequences. That does not make you disempowered, but it does mean you face a difficult situation.

@R1NaNo The reality is that advisors have relatively significant "authority" over students and can, to some extent, decide whether students will successfully obtain their degrees. Although students technically have the right to contact editors, doing so can create an awkward situation. Consequently, students are, to some degree, "forced" to accept authorship. They also recognize that their contributions may not justify being listed as authors, which makes them feel very guilty.

I’m only used to the opposite effect (high level advisors on as "authors" without contributing) but it’s generally not a problem for an individual because "everyone does it" and people have more to lose than to gain by calling it out. What makes you concerned that there will be a (negative) impact for the students? Your description makes it seem as if this is established practice and apparently you are not aware of anyone being (negatively) impacted.

@user_ababa_gwzddx ahh I see, so ones ethics end when it gets awkward? This is not a strong argument. This question is also oddly specific to ones situation and ones perception. The general approach to be generous with authorship towards the contributions of graduate and undergraduate students is typically viewed positively. Often times significant effort can go unrewarded because it does not produce a product, but may be very valuable towards understanding something greater, it is up to the PI to decide where this falls. To mitigate this seek out groups with few authors per paper?

@R1NaNo   Thank you very much for your thoughtful response.
Although the PI decides on authorship, there are cases where a student’s contribution is minimal or nonexistent. Often, students are listed as authors without prior notice or clear explanation, leading to feelings of disrespect. This lack of transparency can cause students to question the legitimacy of their authorship, resulting in significant psychological stress and concerns about unintentional involvement in academic misconduct.

@R1NaNo  Switching research groups is a viable option, but it doesn’t change the existing publication record. Since these papers are usually already published, the student has effectively accepted this 'honorary authorship.

@user_ababa_gwzddx yes, but that is for the PI to decide (impact and contribution tot the project) and the student to decline. Unless there is a case of academic misconduct, there should be no impact to the student in the long term.

@R1NaNo  You’re correct that the decision on authorship and contribution largely rests with the project leader (the corresponding author typically knows best about the paper and who deserves authorship).

@R1NaNo  As for students declining authorship, this can be difficult. Often, they only learn they are listed once the paper is submitted or published. Reputable journals rarely allow the removal of authors during review without a valid reason, which could lead to rejection. This can create conflicts between students and advisors.

@R1NaNo  Removing an author’s name after publication also requires a valid reason and a formal correction. If students only discover their authorship after publication, they can’t refuse without potentially revealing a conflict with their advisor.
Overall, aside from the issue of ‘forced authorship,’ these papers typically do not involve other forms of academic misconduct.

If this is a common practice in some places and with some publishers, then there probably isn't a solution as it would require a change in attitude among a large number of people and organizations. Any solution would probably need to be gradual and long term. Publishers, however, could insist on adherence to rules.
However, in much of the world, this would be unethical, and publishers (reputable ones) require permission of all authors to publish. I would question the ethics of any publisher that didn't adhere to this rule.
I'll assume, however, that where it does occur, the professors adding students to publications are motivated by trying to advance those students rather than to hinder them. If such practices are common then the people remaining in those places probably suffer no harm. It does, however, dilute the impact of the people who actually do the work for those publications. Many people (including myself) would consider that unethical. "Authorship", then, is essentially meaningless.
People who have been improperly added to publications might suffer if the work itself is shoddy. And, cutting corners in one area might be correlated with cutting corners in others. They might also suffer if they try to move to a place with higher standards of authorship and it is discovered that they are actually incompetent in fields in which they have "published".

Concerning your third paragraph, there are some fields where significant effort goes into "behind the scenes" work that benefits every result but none individually. A pure direct authorship model also misrepresents the work of many, or just makes larger endeavours impossible from the get go. However, the fields that I know where this is common usually make it clear that there is a collaboration entity involved, not just a bunch of names.


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My math professor is Chinese. Is it okay for me to speak Chinese to her in office hours?
My math professor is Chinese. She and I both speak Mandarin Chinese (普通话) fluently. The syllabus says that it is good etiquette for everyone to speak & write in English during class, and for all school emails.
Is it okay for me to speak to her Chinese to her in office hours?

This reminds me of my grad school life in the US. My PhD advisor is a Chinese, so am I. In my very first semester in the US, my English was horrible, and so we spoke Chinese occasionally. But she encouraged me to speak English and not be to afraid of making mistakes. So only in the circumstance I really couldn't speak English properly, I spoke Chinese to her.

Ask, but expect a no. My native language is not English, and since moving to the USA speaking that language is reserved for speaking it with my family in the old country.
The context switching is very heavy, and especially and I am pretty strict about avoiding it.

Note that if you get into a habit of speaking with her in Mandarin when one-to-one, this may quickly slip into you also using Mandarin with her when there are other people around. Not only can it quickly become rude, it may also give the impression to others that there could be some sort of preference/bias in your favour due to this.

I suppose the reason for the syllabus rules is intelligibility by third parties (for example, it would be impolite to speak Mandarin (or German) in a classroom full of English speakers, cutting them out; and emails may be forwarded/reviewed etc.). These reasons do not apply to a tête-à-tête oral conversation, so it's up to whether you both agree.

I would ask your professor directly during office hours rather than relying on us.
There may be some good reasons to talk in English besides etiquette. For example, it's good practice in English especially if your education is going to be in English. Even if your English is already quite good, if you'd propose speaking in Mandarin that suggests to me you're less comfortable in English so could benefit from the comfort of practice.

Yes, it should be fine, unless she indicates otherwise. One of the purposes of the rule in the syllabus is, perhaps, for students to gain practice in English. But another is that in public situations (three or more people), a common language is best, and that might be English.
But private conversations are probably different. Ask her if you are unsure.
Another issue, since this is mathematics, with a specialized vocabulary, it is important to learn that vocabulary if you intend to continue in math. That might imply English is the language of choice, or not. I don't know to what extent the vocabulary of math is international.

Math vocabulary IME tends to be more diverged on more basic concepts, and more converged on more advanced topics.

@ave I think that's generally true, but to be clear the terminology at university-level math classes is still quite different in different languages (and not just terms, but also how statements are phrased).

At research level maths a significant proportion of specialized vocabulary essentially only exists in English. If you listen to a research talk in German (which is fairly close to English and also has a rich maths vocabulary) some words will still be in English simply because there is not translation of the maths term to any other language. Example, a line bundle in algebraic geometric may be 'ample', to my knowledge, the math term only exists in English.

This is alluded to in @Buffy's answer, but the answer depends on how well office hours are attended. If you are meeting the professor one-on-one in office hours, speaking Mandarin Chinese does not inconvenience other students.
But in many courses, office hours are well-attended: students come not only to ask their own unique questions, but to hear the answers to other students' questions. In such scenarios, office hours are more like a discussion section, with the topics driven by the problems that students are having with the material. A student might never be able to ask his own question, but that's okay because other students have the same question.
And in such scenarios, it will be highly annoying to other students if you are not using English as a common language. The whole office hour may pass without them ever getting help in their own language.

I would change it from "getting help in their own language" to "getting help in a language they understand"

This is mostly anecdotal, but in my experience, scientific communication happens based on the participants. If everyone speaks the same language, it's that language; if a single one in the group does not speak that language, it's English. I did my masters under a supervisor from my own country in Sweden, while my other two teammates were German and my supervisor was fluent in all three languages. We just kept switching back and forth between them. It's not like it's a big deal - just use whatever is the most convenient. We found ourself, for example, switch to English even if it was just the two of us, simply because it was easier to express science on that language.
Ultimately, it's whatever you two find suitable. Just ask your prof if he'd prefer English or Chinese, and prepare that the answer will be "whatever you prefer.

When non-Chinese speakers are present: Use English.
For one-on-one office hours:

Begin with English, then follow the professor's lead.
If she switches to Chinese, you may do so as well.
If she reverts to English mid-conversation, you should follow suit.

When you're the only one in her office, but the door is open: This is a gray area.
Assuming the professor is a Chinese immigrant, I'm providing a U.S. perspective supported by some data.
Other answers seem to treat Chinese as if it were any other language. From a U.S. perspective, I believe this is an oversimplification. Chinese may be unique in this respect: Chinese people are significantly overrepresented in many fields of mathematics. However, discrimination against them, if not ongoing, is still a recent memory.
I posit that if she has survived in academia long enough to become a professor, she is likely aware of the complexities surrounding language choice. My point is that a Chinese professor may need to consider factors that are less relevant to, say, a Swedish professor. Hence the suggestion: Always follow her lead.

Moreover, if you're talking in Chinese, someone who doesn't know Chinese has no way of knowing if your professor is leaking secrets to an agent of the Chinese government. Yes, this has happened.

Another difference between Chinese and other non-English (but European) languages is, that for example if the door is open and someone overhears you, if you spoke for example German, even if others don't understand it, native English speakers can at least understand the broad topic, the tonality, and the mood of the conversation. Chinese is different enough that they would not even know whether it's a serious conversation, a friendly chat, or a scolding.

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Consequences of registering a PhD at german university?
What does a registration of a PhD at the office of the dean mean? I am interested in both the legal and practical aspects. Of course, there are doctorial guidelines for each university that determine this, but they include little information about what it means in practice.
I know that it is a necessary step to later hand in a dissertation. Also, part of it is that the supervisor agrees to the supervision in some 'contract' that becomes valid once the dean signs it. Moreover, only if you are accepted you are able to matriculate at the university.
What more consequences are there? What obligations come with it? What 'rights' do I get? If I decide to stop this dissertation, is this counted as failed attempt or could I register for another PhD afterwards?
I would expect that there is a good part that is the same for all universities, while details are different. Is this correct? How large is the variance? (Take for example the topic of stopping the dissertation: What is the rough distribution of universities where one could register a new one or not?)
In case it makes any difference, I would be interested in the field of natural sciences.

The details of this will depend on the university and often also the department ("Fakultät"), so it's unlikely that there is a general answer. I suggest that you have a look at the relevant policy ("Promotionsordnung") of the institution you're interested in.

Registering the PhD is mostly a formal act. You get some benefits (such as being a student and thus eligible for student discounts) and obligations (such as having to provide reports) but those are straightforward and should be made obvious by the institution.
The effects can vary quite a bit in one institution (e.g. depending on the domain or funding) so the variations across universities are not all that meaningful. But in the larger scheme of doing a PhD the impact is minimal.
The real restrictions are outside of the formal registration:

At least in natural sciences you will likely be employed by the university. Critically, this is part of your work CV and you are expected to disclose this when applying somewhere else.
There are a lot of soft and social effects at play and applying for another PhD is likely to be disadvantaged. Being younger and not having left a similar program will be a competitive advantage in most PhD applications.*

No matter whether or when you register a PhD, as soon as you actually start with your PhD it’s practically all in.

* Explicitly not in all cases, though.

From my own experience: If you are not employed by the university while doing the PhD, it is advisable to register (and matriculate), just because you can take part in things that go on at the uni, get updates, get access to free ressources like professional development courses etc.
Officially being a student has some perks like cheap public transport ticket and in some cases will give you reduced entries in museums as such, an in a more academic context will allow you to register more cheaply for conferences and give you access to grants for PhD students.
The obligations are negligeable and (for me) do not go beyond paying the small matriculation fee each semester. The only requests I get are from my supervisor, who wants me to keep progress in my PhD - but this has nothing to do with officially registering.
As to potentially negative consequences: I am unsure if people would perceive it negatively would you reapply for another PhD at another uni when having been registered somewhere else before without finishing. In general, my experience in Germany is that your competencies matter more than your matriculation record. And after all, you don't have to put this in your CV and can omit it when reapplying, as the only persons who will need to see the documentation about previous enrollment are the people who work at the matriculation offices.

I would be very careful about the CV advice. Previous PhD attempts are something that may well be taken into account (because there are usually competitors who haven’t walked out of a PhD before). Deliberately omitting part of the relevant information, which would involve making up something else for the timespan, can be grounds for termination.

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I took a summer research intership and didn't do well after, what should I do?
I applied for a summer internship(no salary) in theoretical physics a few months ago, and I got one intership position. After I got the work to be done from the professor, I found that some parts of the project is hard for me to understand, so I ask the professor about it. Only after more than 2 emails did he respond to me with very few instruction, which also helps little. He basically replies 1 time when I sent 2 emails to him, so I finally decide to quit. Since I cannot get vaulable instruction from him.
I want to know is what I did right? Is what I did offending to him? Because I just stop working on it and never contact him again(he didn't ask me either). Should I write an email to say I am sorry this don't work out well?\

"so I finally decide to quit" "I just stop working on it and never contact him again" Did you actually tell them you quit or did you literally just stop working? In other words, are they aware you quit or are they waiting for your next update?

No, I didn't tell them. The professor tell me to contact him again once I figure out the tool for the project, but I failed on that. And just never contacted him. Since there is no group meeting or anything held weekly with which I can talk to him.

I would assume they think I quit, since I guess normally the professor would ask whats going on if he has no response from someone for months... But still, thats what I am uncertain about. I am actually feeling frustrated that time and really hate to contact him again since I think he is not willing to help.....

What is your goal? Do you want to mend your relationship with them for another internship, or because you will encounter them again in your studies? Since you mentioned only email communication, can you actually meet with them in person?

Thats possible, since I am in the same university with the prof.

When you sent your emails. Did you have specific questions? Ones that the faculty could answer to provide guidance? Did you explain what you had tried to do and where you got stuck? Emails that blankly say "I am confused, I don't know anything" are quite daunting for a busy faculty to even attempt to answer. It is clear that you lacked the knowledge to be taking part of this internship, and at the same time, the faculty lacked the time and interest in mentoring someone with so little background.

I have actually write on the tablet to explained my confusion... And he just dont respond

This seems like a very odd unpaid internship. Were you working in the same place as the prof? Were there group meetings? Or were you simply emailing each other, with (by the sound of it) no support one way and no commitment the other?

No place for working, I simply work at home and once I get some result I contact with the prof. "Or were you simply emailing each other, with (by the sound of it) no support one way and no commitment the other? " More or less the truth.

You were apparently unprepared for this internship with too little background. The professor seems unprepared to handle a student with too-little knowledge. This was a bad match, not one made in heaven, but rather...
Yes, I'd send a follow up apology letter admitting your unpreparedness and that you hope to do better with such things in future. Beyond that, there is little to do.
Depending on what paper trail was left by your "participation" you might not need to list this on any CV. If it was "for credit" and you are showing a bad grade, just be prepared to answer questions. "I was in over my head" should be understandable to most (not all) folks. A 2-3 month internship isn't a lot of time to catch up on a deep topic and make a contribution.
There is nothing disqualifying about an undergraduate or beginning masters student being unprepared for advanced work. I'm assuming here that the topic of the internship isn't something covered deeply in your program and that you should have already known the fundamentals.
For future, however, learn to use email less and personal contact more. This will be an advantage both in learning and in keeping relationships alive.

Sometimes personal contact is not possible, the prof may not be available for that. I would also add that one of the most valuable skills is to learn to ask the right questions - find those few questions that give the most information. Many profs are impatient people.

@CaptainEmacs, what you say is true enough, but it is hard for me to have respect for profs that can't make time for their interns and other advisees. And, giving complete answers to a student's email can also be time consuming and not just for the time it takes to compose it.

I was also an intern at some point. I very quickly realized that questions are numbered and focused on learning to pose the most specific and useful questions that would answer as many problems as possible at once. This is a valuable skill to develop, independently of context. Responses tend to be far more helpful, and people ready to help when they notice you've put in your own due diligence in making their job to respond easier.


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NSF Mathematical Sciences Postdoc fellowship: same institution, different department
For anyone who is familiar with the review process of the NSF Mathematical Sciences Postdoctoral Research Fellowship (MSPRF), I am wondering about the issue with staying at the same institution for the postdoc.
I will earn my PhD from a different department, but the proposal (as currently written) is for hosting in a different department, at the same university. I am wondering how detrimental this is in the review of the application?
One potentially mitigating factor is that my advisor moved to a different university during my PhD, so I actually ended up spending half of my PhD at a different place, even though I am formally earning my degree from the original university (the same one I am applying for the postdoc).
Has anyone here been on a panel to review MSPRF? If so, in your experience is an application of this type dead-on-arrival, or is it still possibly competitive. If the latter, what were the mitigating circumstances?
Update: Here is the relevant portion of the application on research.gov

Why do you think it is detrimental? I can't see any issues?

There is a field in the MSPRF that specifically calls this item out, that was the reason for my interest.

The question presumes a negative impact of the arrangement and then asks us to explain. You're getting ahead of yourself. Write to the relevant NSF program officer, ask how to fill out the form, and then ask for any particular tips in making the application successful.

Your proposal will be evaluated by NSF for the quality of the research you are proposing, your likelihood of succeeding in your plans given your track record and other factors,and the ability of your host institution and department to support you.
No one will care that your postdoc institution is the same as your PhD one, that simply has no bearing on any of the above criteria and is irrelevant information.
Edit: okay, I see based on your update that the NSF actually asks for an explanation for these sorts of circumstances, so here are some thoughts about why they are asking and what you might say. My impression is that the people in charge of the program are assuming that people generally do a postdoc elsewhere than where they got their doctoral degree, and that that's the "correct" thing to do for a successful postdoc, but want to make some allowance for the possibility of some applicants deviating from that standard course and still considering their applications and giving them a chance to prove themselves competitive for the fellowship. The extra justification gives the applicant a place to explain what's going on, since without an explanation, staying in the same place seems like a strange thing to do and potentially indicates a person who finished their PhD but doesn't have a really well thought-out plan to take their research into new and exciting places.
A second thing I'm noticing is the language of the form suggests that the people who wrote it haven't really considered the "same institution, different department" scenario, since the form only refers to "place of performance", leaving some ambiguity about whether that refers to the institution or to the department. Thus, in some sense you could quite reasonably state that the "place of performance" for your postdoc is actually different from where you got your degree.
That doesn't mean I advise picking the first option in the form (the one where you wouldn't need to provide a justification) - that could potentially make you look dishonest or evasive if someone notices you're staying in the same institution. Instead, I think the correct thing to do is to pick the third option, and in the "Justification" text box to enter a straightforward description of the situation. The relevant facts are:

you're staying in the same institution.

you'd be working in a different department.

you'd be working with different people, and in particular your postdoc mentor would be a different person than your PhD advisor. (Assuming that's true; if it's not the case and you are in fact planning to continue working with the same people you were working with during your PhD, then you might look like a less independent and mature researcher than other applicants, which would likely put you at a competitive disadvantage.)

etc - any other information that helps explain why your plan to move to that department is good and would lead to good outcomes for your research.

Bottom line, what I initially said in my original answer still holds. The thing reviewers will care about is that your research plan is good, so that's what you should aim to demonstrate, both in your main proposal and in any supporting texts such as this justification field. It's certainly possible to have a good research plan that involves moving to a different department in the same institution. (It's also possible to have a bad research plan with these parameters, and equally possible to have a bad research plan that involves moving institutions.) So again, I think this is a non-issue and will not hurt your application, provided you give a reasonable explanation of the situation.
Good luck with the application in any case!

Thanks. The genesis of my question was that this question is specifically asked by NSF in the application materials---they ask people who are staying at the same institution to justify why they need to do so.

@DrewBrady - well, justify that. You started already with respect to a different department and not truly being there the whole time. No show what the new department brings you.

Perhaps your situation is other than "you need to do so" and it is just an opportunity.

@DrewBrady okay, that makes a bit more sense. Can you post the exact text of the question you need to answer, or a link? That doesn't really change my opinion that this issue is irrelevant to how competitive your application would be, but it's reasonable to ask what is the best way to address your situation in the context of the explanation you are being asked to provide in the application, so I'll be happy to comment on that.

@DrewBrady can you clarify in your question what language you are citing that mentions this?  I'm looking at the solicitation and see as part of the project description: "a justification of the choice of sponsoring scientist and host institution"--this means you have to justify the choice regardless of who they are and where they are.  The idea here is to ensure that you are choosing the right scientific environment to make you successful.  This is reaffirmed in the "Sponsoring Scientist Statement" document.  Also when you want help on a solicitation, you should always provide the exact link.

@DanRomik I have added a screenshot of the part of the application I am referring to.

@DrewBrady, speaking as a research administrator, I would guide you to continue with your plan if you think it is a good one.  You need to have a real explanation for why you chose this path.  In the case of sponsored research, sponsors do allow exceptional things all the time -- it's all about the justification.  Many people do not spend the time to do these sections, and they are eliminated quickly for a lack of a cohesive plan.  If you take it seriously and have an actual reason for your choice, you should explain and be fine.  If you can have a PI review it, that would be best.

Thanks a lot. I also sent an inquiry to one of the program admins to see what they say. However, if I don't get an answer, then (3) is the best option -- "honesty is the best policy."
I think I can easily justify that (a) it is a move to a new department; (b) my sponsoring scientist will be able to help with tracking references, explaining etc. the new tools I plan to use in my research; (c) my sponsor is NOT my thesis advisor (nor is he on my committee, in fact was hired in my penultimate year of PhD). Hopefully this ensures the only thing that matters is the quality of the proposal!


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How can I pursue a career in bioinformatics with limited financial resources and a weak educational background?
I recently graduated from the Faculty of Computers and Information, Computing and Bioinformatics Program at Menoufia University in Egypt. Unfortunately, I feel that my university education was not very strong, which has left me feeling underprepared for the job market.
I am very passionate about bioinformatics and aspire to build a career in this field. However, I face several challenges:
Financial Constraints: I do not have the financial ability to pay for courses or certifications in dollars.
Job Search Struggles: I have been trying to find a job that pays well enough to support further education or training in bioinformatics, but I have not been successful so far. Additionally, I lack sufficient experience in the field.
Educational Aspirations: I aspire to obtain a master’s degree in computing and bioinformatics, but I am unsure how to proceed given my current situation.
Could anyone provide advice on how to overcome these challenges? Are there any affordable or free resources, scholarships, or programs that could help me improve my skills and increase my chances of getting a good opportunity in bioinformatics? Any guidance on how to gain relevant experience or find entry-level positions in this field would also be greatly appreciated.
Thank you for your help! I am eager to learn and grow in this field and would appreciate any support or advice you can offer. Your guidance could make a significant difference in my career journey.

Look into industry internships. And in the meantime learn Bash and Python.

How is it even possible? How can a bounty question be closed?

@pie A couple of months back  the bounty system was adjusted network-wide to allow regular moderation of bounty’d questions. See for example here.

@MisterMiyagi I wish I knew that the question is about to be closed before I put this bounty.

@MisterMiyagi btw why is this question is considered off-topic?

It's almost all off-topic. It starts by listing very specific factors (we don't take questions that couldn't be helpful to future readers), then moves onto a shopping question (we don't recommend resources, scholarships, or programs), and then ends with an out-of-scope question (about non-academic careers). I sympathize with the asker's plight, and wish we could help, but we are not career counselors, and there are good reasons we don't answer questions like this. See the [help] for more details.

It does seem strange that closing a bounty question doesn't result in refund of the bounty rep! Perhaps that's something to raise on the network meta...


text:
I've never been an olympiad-winning excellent student; is there a chance for me in academia?
I'm a woman in 1st year of a mechanical engineering PhD in Europe. I've never been an excellent student in the past (before my PhD). I tried my chances in math and physics olympiads but I've never made it past the school level. In high school, I was in a class with genius national olympiad winners. They could solve the extra homework problems which I couldn't, even after few days. Our math and physics teachers prepared them for the competitions, but whenever I communicated that I'd also like to try they didn't take me seriously. I always thought they don't see the potential in me because sometimes the homework was quite hard for me.
For a long time I thought only those genius kids could do a PhD. But during my master's I got really interested in doing research (or just in being curious about things and then poking those things to satisfy my curiosity) and those interests got me into a good research internship which later got me into the current PhD program thanks to some good recommendation letters. Now I'm in a group with two other PhDs and they both have a long history of academic successes. They seem genius to me. They have knowledge of math which I lack and I have to spend time, a day, sometimes a few days, to understand our supervisor.
Will I be able to stay in academia for the whole career or will I hit a glass ceiling that only a genius could break? If there's a chance for me, what can I do to start to see myself as equals with those PhD students who used to win olympiads in school?

Update:
I suspected that I will hear a lot about impostor syndrome, somehow it's a trend on this SE ;) And I might have one, I don't know; FWIW, I feel good in my PhD, I don't feel that I "tricked" anyone into being admitted to the program. I know I did well in my research internship and that's why I'm here. My supervisor also tells me that she's happy with my progress, so I'm confident that I can complete this PhD.
In my question I wanted to emphasize whether there's a point in an academic career where you really reach a glass ceiling if you're not a genius. I was wondering if as a postdoc or as a professor you really can't go much further if you don't have that olympiad-level cleverness. I now learned from all the great answers that's not the case, which is very reassuring! Thank you all.

Comments have been moved to chat; please do not continue the discussion here. Before posting a comment below this one, please review the purposes of comments. Comments that do not request clarification or suggest improvements usually belong as an answer, on [meta], or in [chat]. Comments continuing discussion may be removed.

McDonalds sells you and me the same cheeseburger that they sell the genius Nobel prize winner.  If your goal is "start to see myself as equals with those PhD students who used to win olympiads in school", you are in for a world of lying to yourself and disappointment.  You are not equal.  But, you don't have to be.  The only part of getting a PhD that has anything to do with comparison to others is getting accepted into a competitive program.  After that, it's about the work you do.  Look for your own strengths.

While in many places, even in the supposedly enlightened developed world, women have a hard road, especially in STEM fields, I don't think that not winning competitions will have any effect on your career. I'd guess that only a tiny proportion of the active users here ever did so and many (hopefully most) are successful academics.
I certainly never won much of anything. And I had a rewarding, if sometimes difficult, career.
Note, importantly, that research in academic fields, isn't about competition. It is about finding interesting problems that are worth solving and finding and publishing those solutions. It certainly isn't about quickly answering questions posed by others where the answers are already known. And answering "tricky" questions isn't proof of genius.
Genius manifests itself in many ways. Winning competitions can also be about luck, when one just happens to have studied or read about the solution to some question posed. It is best manifested in the hard work of exploring the unknown, not quickly recalling the known.
Collaborative work can also be much more important in success than a sense that everything has to be a competition.
I think and hope that the glass ceiling is cracking. But it will be those driven to expand what is known in their fields that will demolish it. It is far less likely to be contest winners. Do good work. Take no crap. I was lucky enough to have had a number of such colleagues. Some have been department heads and deans, recognized for their excellent work.

Answering such questions tends to jog my aging aging memory. I remember that one of the reasons that the other graduate students thought I was brilliant (as they told me) was that I asked so many questions in class. It wasn't that I answered questions, but asked them. My mom, of course, thought I was a pain in the tuchus for the same reason.

Thank you, this is very reassuring and something I really needed to hear to steer my PhD mindset in a healthy and productive direction.

Although I agree with the general answer, "research isn't about competition" is only part of the story — finding a job and getting grant money very much involves competition, although not one where olympiad skills are of much use.

@gerrit It's true that getting jobs and grants are fundamentally competitive activities, but they're also very different from olympiads.  More of the "marathon not a sprint" kind of thing.

@gerrit, don't confuse the competitiveness of academic life with competition in research. The latter happens rarely, though it does. There was a competition for, IIRC, determining the structure of DNA, for example. But job hunts are competitive, certainly. A job hunt isn't research.

@Buffy Fair point.  But to get to do research, one needs to succeed in the competition in academic life first.  And getting awards for papers or conference presentations does help to get ahead in the competition in academic life (but good research is only part of the story for getting such awards).

I'm wondering a lot if I will be able to stay in academia for the whole career or whether one day I'm going to hit a glass ceiling that only someone who was a genius all along could break?

You don't need to be a genius to stay in academia for your whole career.  Excellent maths skills certainly help in STEM, but someone with excellent maths skills but lacking other critical skills might fail, whereas someone with "good enough" maths skills and excellent organisation and people skills might succeed.
True story: a university I know well hired two new professors.  They found two with an excellent publication record, many publications in Science and Nature, and hired them, seemingly impressed that such "successful" academics were willing to work at a not-so-famous university.  The university soon regretted their hiring decision.  The next years were very difficult, as it turned out the two were, to put it diplomatically, lacking in people skills.  After many personal conflicts, stressed colleagues, burned out PhD students and postdocs, and several years later, both left.  Two others were hired who, on paper, may look less impressive when looking at the publication record, but who are good scientists, teachers, postdoc/PhD supervisors, and more interested and successful in bringing the department forward.  The department is now doing better than I have ever seen it, and the "crisis years" a lesson and a time of unhappy anecdotes that soon only the senior faculty and administrative staff will remember.

And if there's a chance for me, what can I do to start to see myself as equals with those PhD students who used to win olympiads in school?

Academia is not about winning olympiads.  To succeed in academia, you need to be a good scientist.  You need to be well-organised and have good people skills, and it helps to be a decent teacher.  Olympiads are at best partly predictive for the maths skills needed in STEM, but have probably no predictive value for any of the other skills you need to succeed in academia (nor in other careers).

(+1) "The next years were very difficult, as it turned out the two were, to put it diplomatically, lacking in people skills." Geniuses are notorious for their lack of soft skills.

Olympiad winners are more like sprinters, whereas success in academia requires more of a marathon runner. It is by no means guaranteed that a successful olympiad winner will have the stamina to complete a PhD project and the patience to write a 100+ page thesis. Slow but deep thinkers tend to fail in olympiads but succeed in academia. There are also psychological factors that favour success, such as being a team player rather than an individualist, or the ability to avoid conflict. Also, the number of unsolved problems by far exceeds the number of geniuses, so there is always plenty of work for everyone.

"The number of unsolved problems by far exceeds the number of geniuses, so there is always plenty of work for everyone." That's a great thought that I shall keep in mind, thanks!

I too have had colleagues that I consider genius. It's easy to feel like an imposter in such company when your history of grades has been good but perhaps not at the very top.
But then I got into a Ph.D. program (in experimental physics) and started going to conferences. There, I saw what the actual standard of research is: The vast majority of work was incremental stuff I could understand and imagine myself doing (with a bit more specific experience, of course). Even many "breakthroughs" were either chance discoveries or the culmination of a logical series of incremental advances. Pure works of genius, i.e. tours de force of breathtaking creativity and/or intellect, did exist, but they were few and far between. Plenty of people seemed to be making nice careers out of the (hard and plentiful) work of further developing someone else's breakthrough. The breakthrough is just the beginning.
All of this is to say that genius is nice, of course, but in academia it's neither necessary nor sufficient for success. There are many skills needed for research, and they strongly depend on your field. In most cases, the skill of solving tricky problem sets (timed, with limited resources, and where you know there's an answer) wouldn't even rank in the top 10. Real research is different, and you get to choose your field to suit your skills.

(+1) for The vast majority of work was incremental stuff [...] Even many "breakthroughs" were either chance discoveries or the culmination of a logical series of incremental advances

There are two major effects in your favor here.
First, being good a math competitions is only loosely correlated to being good at math research. Some skills overlap but a lot of others do not and there are plently of people who excel at one but not the other. The other two answers focus on that aspect in more detail.
Second, there is the quantity of people. There are a lot fewer people who get prices at high level math competitions that people who do successful math PhDs. Even if you assume that the national olympiad winners are better than you in research math that does not imply you are not qualified to become an excellent math researcher.
Personal anecdote: I went to a high school with a strong math focus, well known for being very successful in high school level math competitions. Several students who were maybe slightly below average in math in that specific school had these eye-opening moments when they started college, their math skills were easily sufficient to become very successful mathematicians. So morale, there are always people out there who are better than you. This does not imply in any way that you are not good enough.

While I can't speak for ultimate academic longevity, plenty of people in my department (myself included) never really got into Olympiads. That doesn't make them dumber, or worse academics- the skillset you need to be good at specific styles of problems isn't necessarily the same you need in STEM academia in general. While people start with different levels of innate talents, usually by graduate school, most need to start putting in the hours and actively learning skills.
Since you're just starting out, some miscellaneous points from a non-genius who completed a PhD in STEM in Europe:

Weight other people's opinions of your academic potential based on their qualifications. You mentioned that you think your high school teachers didn't believe in you, yet you have multiple positive recommendation levels from people who have actually worked with you in a research context. I would give more credence to the latter.
Avoid trying to compare yourself based on being the "type of person" who succeeds at X. We all know various stereotypes of the "genius scientist": some people will fit this more than others, but arguably every person is a bit more nuanced than that. There's also strongly gender/ethnicity related aspects to this question that I don't want to start a flame war about. But to keep it short, don't waste your time thinking about if you're the kind of person who can do X: just give X your best attempt.
Try to avoid putting other academics on a pedestal. Yes, plenty of them are incredibly bright. But these are also now your coworkers, and they're still normal human beings. Have some coffee with them, and chat about your projects. You're not in a competition about who is the most smartest most deservingest with the bestest brain: these are potential collaborators, maybe friends (the other students) who share a lot of interests with you.
Things will get better: I'm not saying your advisor isn't a genius, leaving cryptic messages for other geniuses (genii?) to decode in a bizarre merit-competition. But to play the devil's advocate here, maybe they were someone very smart who worked very long hours for very long years on some specific topics, and now is very good at them, to the point where they sometimes lost sight of what is obvious. Once you've settled into your PhD, the methods you work with will also be "obvious" to you, and you'll seem like a genius to the undergrads.

As a corollary to the last point: if you're feeling impostor syndrome or insecurity, it can be helpful to have others to talk to who have also struggled. Are there any ways to meet other new students? Especially at the start, it can feel difficult to admit (perceived) weakness or failures, even though most everyone will face some sort of challenge in their PhD at some point. Finding some community can be very helpful here.

I was a (quite) successful Olympian and my MSc was not difficult. MSc is the level where you get a grasp of the topics of your studies without going into scary details. I loved all my courses.
Then I went for a PhD. It was something really different - I had not only to learn but also to discover something. That was quite a change because this something (the core of your thesis) is not written anywhere and obviously people do not know about it.
What I am trying to say that learning, teaching and research are really different beasts (though teaching is awesome for learning). They use different muscles in your brain and some have them, while others do not (everyone has some of them but you never know until you try).
A PhD sounds grandiose and sometimes is. But in everyday life there is a lot of wandering around, thinking and sometimes you get an eureka moment (and the day after you realize that it is wrong).
I do not think that you can "guess" that you will be good or not during your PhD studies (as opposed to MSc) but if you are curious and love your topic go for it. This is a wonderful time in your life, a once-in-a-lifetime experience. Do not forget to party.
PS. After getting my PhD I realized that I would not be a good scientist after all and moved away from Academia. I sometimes miss the teaching and the joy of doing research though.

I hope I didn't miss anything, but I want to list two important factors I haven't seen in the other answers.
First, being able to find unsolved problems or that certain kind of creativity to realize that you could apply method A, that has previously been used on topic T, to your topic S and get amazing results. Winning an olympiad means the others were able to solve problems they are given, but that doesn't mean they are able to come up with problems themselves. If you go to industry, you might be perfectly fine solving problems you are given by your boss or your customers. In academia, this might be enough to get a PhD, but after that, you are expected to find your own topics.
Second, networking with others. Discussing with other researchers in your group or at a conference / workshop. "It's no competition" has been mentioned a few times, but I think it's actually more than just that. Not only can more than one person work on a topic, but the best ideas often come from interaction with others, when you share your findings and suddenly realize that the other party has / knows the missing piece to your problem. Not to mention that hiring commitees love people with active cooperations so they can "brag"...
I once had a colleague / fellow PhD student who was much better than me in these two aspects, while I was the one that had received better grades in most classes. She's a professor today while I'm a teacher - and while that seemed to be a breeze for her, I'm pretty sure that I would have been up for a rough time finding a tenure track position in academia (plus, I always liked TA-ing much more than research, so I consider myself in the right place now).

As Thomas Edison so famously said:

Genius is one percent inspiration, ninety-nine percent perspiration.

He went through literally thousands of different concepts and materials in his quest to invent the light bulb before discovering something that actually worked.  The (arguably) most transformative invention of the modern era was not some grand idea that sprung into the mind of a savant, it was the product of many years of hard work, a continuous process of refinement and experimentation.  Edison isn't unique in this, either.  The same is generally true about just about any great invention or discovery that you can imagine.
Your colleagues aren't more capable than you, they just have a different set of strengths and weaknesses.  The best teams are made from people with diverse skills and strengths. Chances are, they'll end up feeling the same way about you because there are things you can do much more easily than they can.  You're not competing against these people, they're your teammates.  Learn what each person's skills are and use that to help advance your team's objectives.
In the end, it's all about doing the work.  Research is work.  A lot of work.  Being a stereotypical "genius" might make some things easier, but success is ultimately down to putting in the work.  Work hard and do quality work, and I think you'll be surprised at how far you can go.

Our math and physics teachers prepared them for the competitions, but whenever I communicated that I'd also like to try they didn't take me seriously. I always thought they don't see the potential in me because sometimes the homework was quite hard for me

Well, have you ever considered why you wanted to try? To me, it appears as if you were trying to prove yourself, and this is dangerous when you don't recognise that "deep" talent usually only comes naturally. I think it was more about your own self-esteem, than about them not taking you seriously. What is serious, is that they should have sat down and discuss with you about it. Trying to reach ceilings is OK, but trying to break them often causes pain. You can avoid this pain when you don't set out to "break ceilings" but just try to be the best you can be, and it may also lead to some broken ceilings along the way ;)

You appear to be overthinking it a bit, which undoubtedly is causing you the stress that motivated you to write this question.
There is a number of reasons to pursue an academic career; they generally fall on various points on the broad spectrum of egoistic vs altruistic:

For the money?
For the fame?
For the fun of it (common motive of geniuses)?
For the security it brings?
For the passion/lust of it?
To advance science and humanity?
...

Depending on the percentage breakdown of the reasons you're in it for, you will have different obstacles hindering your "psyche". In my experience, being somewhere in the middle can definitely down-regulate your feeling of difficulty, but at a proportional cost in your feeling of achievement. In short, rationality can be easier exercised over emotion when in a balance between egoism and altruism.
When you lean to the "sides" of the egoism-altruism continuum, (i.e. much more egoistic or much more altruistic) you have more motivation and can bypass issues more easily, regardless of the size of their actual "footprint". Your emotional strength will help you go through perceived problems more easily, and will not feel the need to intellectualise or even rationalise your conflicts. You will be OK with your sense of self.
The trap, though, is that as you lean towards more egoistic motives in contrast to altruistic ones, you will be more prone to distortions in your perspective, closed-mindedness and immature psychological defenses, i.e. your strong emotions will dominate your pursuits more than rationality. Your enjoyment will then concentrate on the achievements themselves, i.e. the fame, the money, etc, so you will have to wait and fight hard until you get there. Valuable friends are also much harder to make in this modus operandi, I am afraid.
When your motives are mostly altruistic, your enjoyment will be distributed all along the path. This, again, in my experience, starts from day one. You "risk" feeling more content, as you will find satisfaction in much more than just the "goals". You also "risk" being more open to requesting or providing help, viewing problems more optimistically and, maybe somewhat unexpectedly, actively be on the look for "ceilings to break". You will most likely not break any ceiling alone, and not become a household name, but you will also feel you don't need to. Altruistic researchers generally mostly enjoy breaking ceilings together with others and find satisfaction in the process much more than only in the results.
When you reach a fair level of maturity in your academic pursuits, you will eventually understand that what now view as "geniuses", is actually going to be one of your best "tools", to achieve multiple ceiling breaks at once. Truly competitive geniuses are very easy to manipulate, so please treat them with responsibility and kindness when you get there.

Update
In reality, you are asking whether there is a point to get into the fitness/training market if you cannot run as fast as Usain Bolt. Well, what do you think?
True satisfaction comes only by accepting who you are, and trying to be the best you can be, with a reasonable effort. You are not going to be very happy trying to be the best others think you can be, or the best you see in others.
TL;DR
You are going to do great. If you are already a PhD student, someone that has also been trusted in the past to get into academia, also trusted you. Transitive trust is multiplied trust!
Enjoy your efforts!

Thank you for your answer, I must admit that I don't have enough research context yet to fully take in what you're saying. When you say "what I now view as "geniuses", is actually going to be one of your best "tools", to achieve multiple ceiling breaks at once" do you mean that once I become a supervisor myself I will be looking to hire those geniuses to work with me?

Well, if we take it to the extremes, somewhat, you are correct. However, what I mean is that you are often going to be working in teams, and "geniuses" in a team are a double-edged sword. Internal competition hinders progress, and a genius can quite often get on your nerves. If you learn to view geniuses as a great opportunity to work with, despite the difficulties that may arise, you may realise that they also need some assistance with "steering" as they often tend to focus very much on what they can do well.

You will most likely realise as you mature, that you can utilise their strengths and direct their efforts more effectively than they can do on their own. Progress is not based on isolated "wonders", but in joint efforts to merge their capacity. Being very good myself at the things I have undertaken in the past, I realised that I ended up being like a lonesome cowboy, struggling to achieve results of multiple people together, and feeling exhausted after years of doing so. If I only had one piece of advice, that would be ...you really don't want to be a genius!

You are only going to be happy as a genius as long as you don't realise exactly what you are (i.e. a one-man show that everybody views as unique). Learn to love your abilities, hone your skills and don't overdo it. Academia is not about exploiting your skills, just being the best you can be and finding ways to exploit your strengths and tame your weaknesses. Actually, most occupations are pretty much like that. I have also updated the question slightly, check it out if you'd like ;)

Thank you for leaving me with many interesting thoughts! I really appreciate it.

You are most welcome, and don't hesitate to ask anything here!

Good news and bad.  First, I wouldn't put too much weight in the Olympiad stuff.  If this makes sense, success in that sort of thing can be a predictor of real life success in a person who also has grit, determination, and a good work ethic.  But lackluster performance in such things is not a predictor of failure simply because there is so much more needed in life than is needed in one of those exams.
The bad news: I was actually a far and above average student all along, and I dearly wanted an academic career.  I didn't make it.  Several years ago, staring at a choice of the corporate world or unemployment, I went corporate.  The competition for academic positions is fierce, probably because they are just such plum positions.  You don't get filthy rich but you will get paid very well to do stuff you would do for free.  I would suggest that you talk to some professors and researchers who have made it.  Ask them what they did to make it and then ask yourself are you able and willing to do the same?
Also, you probably have a student membership in a some relevant professional organization (or you should) and many of these orgs publish annual stats on how many jobs were open last year, how many people younger people stayed in academia and how many went elsewhere, etc.  Get that information in hand and then make an honest assessment of your chances.  And good luck.


text:
Considering a Program Switch
I'm currently in my 4th year of a CS PhD program in the US. I've completed most of the program requirements (coursework and qualifiers), so now it's just my thesis remaining. My work is about 80% Math and 20% CS, and my advisor is from the Math department, though she has a courtesy appointment in CS. When I started working with her last year, she suggested I take some Math courses to support my research, which led me to realize that I enjoy the Math aspect of my work more than the CS side.
In fact, I've come to prefer the Math culture over the CS culture at my university, and I find myself more engaged in Math seminars than in CS ones. I also feel I'd be more comfortable as a Math faculty member if I end up in academia, particularly in teaching Math courses rather than CS.
I've been thinking about discussing this with my advisor to see if there’s a possibility of switching to the Math program while keeping her as my advisor. I realize this could add around 1-2 years to my graduation timeline, as I'd need to complete the Math department's requirements, including qualifying exams and some additional coursework (some of which I’m already considering for a master’s in Math if I stay in the CS PhD program). My goal is to pursue a career in academia, and I feel this switch could improve my chances of joining a Math department, where I believe I’d be happier.
On the other hand, I recognize the challenges of securing an academic position, and if I ultimately need to transition to industry, a CS degree might offer better job prospects.

This is too individualized for us to answer here.

I'm not in math, but from what I hear, the competition for math jobs is fierce, whereas CS is maybe a little bit better because people have industry to go to as well. I probably wouldn't move from a degree that opens more doors to once that's less for the cost of 1-2 years.

As a follow-up to @AzorAhai-him- 's point, I answered a different but related question recently that I think contains relevant information for you. Long story short, as a strategic matter, it seems to be a lot more lucrative to have CS credentials as a theorist than math if you're at this intersection. Yes, you may have to resign yourself to teaching CS-type courses for undergrads. But that's just as likely to happen in a math dept. https://academia.stackexchange.com/questions/214545/working-as-a-computer-scientist-with-a-research-focus-purely-in-pure-mathematics/214628#214628

See also our canonical question on switching fields post-PhD (which, IMO, is a better idea than switching during the 4th year of PhD).

What’s your actual question? It looks like you are only describing your situation, not asking anything.

Let me suggest an alternative that will, perhaps, get you started earlier, but to the same place, more or less using those two years.  Just something to ponder.
There are certainly parts of CS that are a lot like math, in theory and in practice; similar methodology. You seem to be in a good place with a good advisor at the moment.
Suppose that you focus in your dissertation research with this advisor on one of those "mathy" aspects of CS, finish the degree and find a job in a department that values theoretical work.
Suppose that you keep in contact with this advisor after completion, and become her collaborator on something even more along the lines of math. A background in CS might be helpful in some aspects of math just as math is helpful in parts of CS.
I don't think you need a MS in math to take this route but it will take some study. Your current advisor might be a good guide for that.
There is no reason why your career can't take a turn.
You don't seem to be absolutely driven to math with no other mental options, as many of us were. We often say "You don't choose math. Math chooses you." Unless that is truly the case, some flexibility in the early career with a press toward a wider goal might be a good move. It won't work if math has truly "captured" you, but it might otherwise.
Some but not all universities have math and CS departments that interact with one another. Someone with a foot in both camps can be an asset.
Of course, you should follow a dream, knowing something of the consequences. As noted in comments the market for mathematicians is pretty tight at present.
My career was in the opposite direction. I was "driven out" of math by an extremely unfavorable marketplace and switched to CS with self study. I was comfortable there, both with the more and the less theoretical aspects. Oddly enough, one of the authors of the first book I chose to study programming (a luminary) later wound up  as a student in a short course I gave at a CS conference. Quite a coincidence.


text:
What is the general expectation for the availability/retention of the experimental data?
A statement from this answer has caught my eye:

certainly you can't be expected to hold onto old datasets in perpetuity

So far, this is exactly what I have been striving to do, to the best of my ability. It is often impractical, it consumes a lot of resources (time being the most costly one), and pragmatically, I am near-certain I would never ever have any use for much of this data.
However, ethically, I consider it to be a part of the burden associated with the job: no one likes chasing apocryphal datasets and results, and sometimes new ideas require revisiting decades old work. Assuming the lack of convenient options for archival, what is the reasonable retention time for the experimental datasets? 10 years? 15? 30?
What would be the guiding principles to determine it?

I think this rather depends on the type of the data set, but in the current age where many data repositories are freely available, why not publish the data if feasible?

@Anyon: biological, medical or psychological data often has strong privacy implications. (Fun fact: maybe you collected data ten years ago under then-legal privacy and data protection rules, which nowadays are not OK any more... but you may need to hold on to the raw data ten years beyond the last publication based on them, which might only have happened recently.)

Is it really that impractical? In my experience, storage prices have been dropping faster than how fast the rate at which I produce research data grows. This means that the older the data, the cheaper it is to hold on to it, to the point where it's cheaper to just keep it indefinitely than to think whether and what could be deleted. A year's worth of my raw research data from 15 years ago is just a couple dozen gigabytes, and that's what I produce in just a few days nowadays.

@TooTea buying personal storage is easy.  Getting storage hosted by a university (unless it fits into a framework they already have, and many aren't at all flexible) can be much harder, and it's the uni's data.

I have several boxes of 5" floppies from way back. They are available. They have been retained. Getting the data off them might be a slight problem...

@Anyon Well, to give an example - Zenodo's size limit for a dataset is 50 GB or so. This is somewhat typical for the freely available data storage, it's not too hard to store up to a ~terabyte, but past that, it tends to be problematic. And, as other commenters have pointed out, there are other potential problems with off-site storage.

@TooTea Depends on your threshold for practicality, I suppose. Perhaps the most important in my specific case is that much of this data was acquired using some old, now-phased-out hardware; we have better devices and procedures now. But if anyone's to ever access it, I need to maintain the entire relevant stack. Even without that, procuring a mere 10T worth of storage with at least a single back up in another physical location can involve a whole bunch of red tape. We also seemingly got on the big data trend early, so 10 years ago it was already ~0.5TB/year, and grew slowly since.

@Lodinn Hence the "if feasible" part. That said, sometimes publishing/retaining some processed data (reduced for size, anonymized, etc.) is the best that can be done. That's part of what I meant by "depends on the type of the data set". Practical and pragmatic considerations should be factored into the expectations in my opinion.

@Lodinn As far as archival goes, I'd just store the raw files and forget about the rest of the stack. That's probably the most cost-efficient option, because with a very slim chance of the data ever being needed again, it makes more sense to potentially have to pay thousands to rebuild the stack at that point if needed than to maintain it all the time. 1T of archival storage (e.g. S3 Glacier archival tiers, no endorsement) can be had for just a few €/$/£ per year, so some 100T costs a tiny fraction of the yearly budget of a typical lab.

I remember what Mrs. Grill taught us in 7th grade:  never throw out he primary data

You should consult the policies of your institution and your department. Some require lodging data in an archive. It might not be perpetual but it is close.

The job of archiving stuff is best done by professional archivists. There are various archives that focus on preserving data and making them available in a legal and ethical way, e.g. ICPSR in the USA, Gesis in Germany, DANS in the Netherlands, etc. Go to the website of the archive that is relevant to you, and you will find instructions on how to submit your datasets.
That way there is a legal department that takes care of all legal problems that may arise. If 40 years from now nobody can read the dataformat of your dataset, then the archive has people specialized in reading ancient file formats. If you are no longer alive 40 years from now, the archive still exists and your data is still available. So this is not something you should try to do yourself.

A good starting point would be your funder's requirements. In Germany for example the German Research Foundation (DFG) funds a lot of university research. Its guidelines for handling of research data state that data should be stored for at least 10 years (see their guidelines).
In fact, I have had a lot of projects funded by the DFG, but they never checked on our research data management so far. Also, although you have to include a statement about data handling in proposals, my project partners rather often do not take this very seriously, and also the reviewer reports never mentioned anything critical about it. So for all practical purposes, my take is that we are still working towards a standard best practice.

UKRI on the UK also specifies 10 years.


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Will bad english grades in high school severely effect my chances of becoming a scientist
I have got bad grades in high school English (Ds and Cs) but if I'm interested in something and have a good idea of the writing style and subject I can write well enough. so I want to know how severely this will affect my scientific career

Perhaps, in the near future, you will be able to write flawless English (with the aid of a computer).

It’s too early to tell but work to improve. Sorry but this is off topic here

Being a successful scientist involves a very large amount of writing about things you are not interested in.

Well, bad spelling is more likely to affect your chances than to effect them.

Are you in university yet? Undergrad admissions is the last time that anyone will care about anything related to high school. (Although being able to write well will become an increasingly important skill, so maybe sign up for some technical writing courses if your troubles were with writing rather than, e.g., overanalyzing literature.)

High school grades are relevant to entry to university, so bad grades may make it difficult to gain entry to a degree program.  However, if you can get entry to a science degree (or related degree) program then you will have a substantial period of time to learn the skills needed to have a scientific career.  Your high school grades will become less and less relevant as new learning and course outcomes supplant your older grades.

...but if I'm interested in something and have a good idea of the writing style and subject I can write well enough.

No, you can't.  If you could write well enough for professional scientific practice then you would be getting As (with ease) in high school level English.  Take your skill deficiency seriously and work on it throughout university.

Writing is hard. Good, clear, precise writing is harder.

I wouldn't take high school grades (low or high) as an especially strong indicator of technical writing skill. It probably depends on the school, but my high school English classes involved a lot more than just writing, and the writing itself was of a rather artificial "high school essay" style rather than anything that would be useful for scientific writing. (The question itself suggests that the OP might be a big careless with punctuation, but other than that, it's not too bad. Which isn't to say they shouldn't work on improving their writing, but they'd need to do that in any case.)

No one will care about your high school grades after undergraduate admissions.
But, whatever caused you to get those poor grades will not be magically left behind and could easily carry over into your undergraduate career which will then carry on to the next steps to some extent.
So, don't focus backward on the grades themselves. Do focus forward on whatever you need to change in your learning or work style to perform better in the future.


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Returning to graduate school after an unsuccessful PhD
In 2020, I enrolled in an American PhD program in pure mathematics, which went very poorly with no one to blame other than myself. I didn't accomplish very much or build any lasting relationships with the faculty there: I spent the first year doing coursework remotely during a pretty devastating mental health spiral, and ended up getting hospitalized and taking a leave of absence (which has since led to me fully dropping out of the program) during the first quarter back on campus.
I've mostly accepted that this sequence of events has foreclosed any professional involvement with academia, and have done my best to move forward with my life and find other things. It's still a singular passion for me though, and losing my chance to participate in research mathematics so early on in that process is something that I know I would deeply regret.
I'm considering reapplying to graduate schools during next year's admissions cycles, but I'm not sure if "unceremoniously dropped out of a previous PhD" is something that I can feasibly overcome in the admissions process, especially since I have no one I can ask at that institution to even write me a letter of recommendation.
How realistic is it to pursue another funded PhD? If I do decide to have another go at academia, does anybody have any advice on how I should approach that process differently given my situation and history?

There was the whole Covid thing which impacted many people. Go for it.

You will need letters from people who can honestly predict your success. Do you have those? Can you develop such relationships?

Do you have a masters? If so, maybe try to go for a masters program first and then after finishing that go into a PhD program.

‘I struggle deeply to imagine long-term happiness for myself that doesn't involve some participation in research mathematics.’ I think you’re selling yourself short, imagining your future happiness depends strongly on, what’s often, a crap-shoot. Don’t do that to yourself.

Yes - you should keep in mind that a majority of PhDs in mathematics write 0 or 1 papers in their entire careers.  Getting to continue to participate in research mathematics after finishing a PhD is against the odds.

This question is similar to: How are Ph.D. applications evaluated in the US, particularly for weak or borderline students? Am I likely to get into school X?. If you believe it’s different, please [edit] the question, make it clear how it’s different and/or how the answers on that question are not helpful for your problem.

If at first you don't succeed, try try again
Failure at a thing once does not preclude doing better at it in a subsequent attempt and ultimately succeeding (recognition of which is virtually the entire basis for education).  As far as admission to a new PhD program is concerned, you need to consider a few (related) things:

What, if any, changes have occurred in your life and/or life skills such that you would now either avoid or successfully manage the mental health issues that were causing you grief?

What, if any, changes have occurred in your life and/or life skills such that you would now succeed in a PhD program instead of failing?

Can you plausibly demonstrate any of these changes through some participation in courses and/or research training at a lower level than a PhD program?

Can you (either now or after that) plausibly convince any of your potential referees to write you a letter of recommendation that will indicate a reasonable prospect of success?

As to your broader desire and passion to do research mathematics, you should bear in mind that this is not synonymous with an academic research position.  Modern academia is extremely competitive and very few PhDs get research careers in academia, so even if you complete your PhD successfully there is a reasonable prospect that you might have to do research mathematics only/primarily as a hobby rather than as a career.

Great answer. Here's one interesting data point on failing and then succeeding: this guy applied to become an astronaut three times, succeeding on his third attempt.

You have to remember that a PhD is one of the hardest intellectual and psychological challenges you can face. This is even more true if the program was remote, which we all know has a higher impact on our mental health, and if it was during COVID.
Moreover, building connections is harder if you work remotely. My personal experience is that building connections in academia is generally hard. Many researchers are willing to connect only if you have some work they can take part in or if you have reached a certain status (h-index, published in a specific conference, you name it).
I'm a PhD candidate in computer science, so I can't say much about your field. However, I can say that this is my second PhD. I dropped my first position (after two years) to pursue this one in another country, with better funding and a better institution. The admission office was really understanding of my situation, and I just tried to be upfront about why I was moving on and why I wanted to give up on two years of research. Also, what helped me a lot was that my current supervisor agreed with what I was doing and supported me throughout the process.
Don't give up if you feel that academia and research are what you are looking for. Maybe try to find a program that better suits you, or perhaps one that can better assist you so you don't face the same things again.
(I didn't want to repeat any other answer points, but they are all valid. I just wanted to give the perspective of someone who had a bad time during their first PhD and moved on)


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One-sided vs two-sided hypothesis testing - Should the hypothesis reflect this?
I had written a hypothesis that we expect x outcome variable to increase when y treatment is applied. A reviewer commented that based off of the hypothesis, we should be doing a one-sided test.
However, this doesn't make sense to me, since it is important to know from the data if x actually decreases when y is applied. Wouldn't doing a one-way hypothesis miss this?
If the written hypothesis must reflect what kind of testing was done, how should it be written to prevent it being boring/uninformative? For example, "We expected x would change when y is applied".
Thank you!

@EdV I think that this is sufficiently divorced from the contents of the OP's research to be on-topic. Its relevant for anyone whose research involves hypothesis testing.

@nick012000 Good to know: the boundary between on topic and off seems to be fractal. Anyway, Buffy’s answer is correct and the choice of one sided or two sided gets done before the testing.

This is probably on-topic on crossvalidated.stackexchange.com I don't think it's on-topic here, as it is fairly technical and substantive.

I was interested in the writing aspect - how to write the hypothesis at the end of the introduction. However, from Buffy's answer I see that I was missing the connection between the hypothesis I set out at the beginning and how it's written up.

This is opinion based.  Even if we knew exactly what the research was, it might still be opinion based.  I do not see any reason not to do it the reviewer's way.

If your hypothesis doesn't match your test, how can you say that carrying out the experiment gives you anything valid. Make them match. If you want a two sided hypothesis, use a two sided test. Similarly for one sided.
Of course, a close examination of the data might reveal whether the preponderance is on the high or low side, but that isn't really using statistics correctly.
Make them match. If you expect an increase, then test for that. If the test fails then reevaluate your assumptions and test again.

Right. To echo: claiming to be testing for several possibilities all at once is like p-value hacking and such: a mis-use of statistics.

Thank you - this clarifies things for me. I think I missed the connection between the hypothesis I set out with and then what gets written in the manuscript. I appreciate the clarification!

@DanielHatton, I think you're right, but/and people operating in ignorance of such considerations have a big chance of messing up... Awareness of the pitfalls, I think, is important, and not reliably "publicized", it seems.

@DanielHatton For one thing, I think p-hacking often implies not including all the relevant data in the analysis, which is a major problem for a Bayesian. Anyway, on the inference side, I would say that the Bayesian way to compare hypotheses is to construct odds ratios. If you try N hypotheses that you consider to have equal prior odds, then the log odds is just the difference in the log evidences for each hypothesis. With p-hacking, you end up with N hypotheses that don't really have equal prior odds, but were chosen after looking at the data, and on top of that you only keep 1.

In other words... say we use p-hacking to confirm hypothesis H', which we claim in a paper was a good fit to some data. If we did a true Bayesian analysis, the honest way to confirm H would be to have decided on hypotheses H1, H2, ... HN, H' to test, and assigned prior odds to each one, then we do a measurement and compute odds ratios for each pair. But if we look at the data before formulating the hypotheses, this invalidates the whole procedure. This is a big problem if, before we looked at the data, we would have assigned H' a much smaller prior odds, than we did after looking at the data.

True -- although I think the problem with p-hacking is that you end up using an analysis procedure / hypothesis that in advance you would have said was contrived (with low prior odds), but that you suddenly after the fact treat as having high prior odds because that hypothesis has a numerically large value of the evidence. So, it is cheating to get the answer you want. OTOH I agree if you do everything honestly, there is not really a way to "p-hack" as a Bayesian; if some weird hypothesis has enough evidence from the data to support it, then that may well be the right conclusion.


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Should I add a person who left my group to my evolved paper?
The answer, in theory, is trivial -- add them if you think they contributed -- but there are some nuances...
1) This person (who left about 4 months ago) was a more senior researcher (not Prof. title, in fact below the Prof.) who was in charge of managing a small research group that I'm part of. This person expected to be in the author list of all papers, given the seniority, even with minimal contribution (1h meetings every 2-3 weeks where basically I explained what I was doing and was given some high level comments)
2) We did have a couple of meetings prior to the departure, where the idea (the 'direction', the 'goal') of the research was agreed upon and how it could be tackled.
3) We agreed on a possible solution that I proposed, but this solution did not work (the person is not aware of this, since we haven't been in touch since the departure) and the paper is now quite different. Even though the technique I'm using now was brought up in a conversation, I had already had the idea that it could work so it was not this person's contribution (although the person might think so because it was 'said first').
4) We have had previous arguments over paper authorship, and I gave in and added the person just to be in their good graces.
I do not think this person contributed enough, given that the initial idea was mine, the solution was mine, the paper and experiments were done by me. But I don't want another enemy in academia. At the same time I have negative emotions toward this person because I was on the receiving end of some name calling and threats during a previous argument that affected me negatively.
Thanks for any input.
EDIT: I'm a postdoc.

".. don't want another enemy .." (italics by me) Ouch.

It is good to minimize the number of academic enemies you have. Purely as a strategic move you might just want to ask them unless it seriously offends you. That seems better than making an assumption one way or another.
How much contribution is enough depends on a lot of things, including field and place, of course. But helping with "high level" direction doesn't sound like it is trivial.
Of course, in asking if they would like to be on the paper you could say that the "paper has evolved quite a lot since you were last involved".
Pick your fights for when they really matter, I think. Especially as a post-doc who wants to move up.

Supposing potential coauthors are not for or against the coauthorship and you do not think that either approach is non-ethical (see the Vancouver conventions below for a widely used necessary and sufficient criteria for authorship; your field may vary), send an email and ask them. Have the paper draft (with or without their name) as an attachment.
Dear title name,
We have continued working on the paper after you left. Here is the present draft that we are considering submitting. Would you like to remain/be an author and if so, could you read through it and suggest improvements?
The idea is to ask if they interested and ask for input at the same time. If they do not answer, then after reminders and other good faith attempts at contacting them, they have not given their final approval and you do not need to include them as an author; do acknowledge them as appropriate, of course.
For reference, Vancouver conventions, from https://www.etikkom.no/en/library/practical-information/legal-statutes-and-guidelines/the-vancouver-recommendations/. These need to be interpreted charitably to make sense for less experimental fields.

Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
Drafting the work or revising it critically for important intellectual content; AND
Final approval of the version to be published; AND
Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

The best advice is to be professional. The professional thing to do is likely to add this person as an author for your stated reasons given that they did contribute to the paper.
I had a similar situation arise with authorship a few years ago. My coauthor and I had a falling out. Prior to this we were working on a manuscript where we agreed to both be first authors. After the falling out, I had the opportunity to list myself as the only first author. At the time, I felt very indignant. Later I realized that the fault for the fallout was largely of my own making.
I am thankful that I stayed professional and cringe at the idea of not being professional now that the emotional cloud has lifted away. I would have gained nothing by doing so and only lost. Keeping the author order as agreed allowed things to normalize again.


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No response from professor for letter of recommendation
I am applying to MS/MA programs and my first due date is tomorrow. All my matierals were submitted 2 weeks in advance,however, my final letter writer hasn’t submitted his LOR. My other two have both been submitted but I am worry they will not carry as much weight as they are coming from directors at my company (I have been out of school 3 years). My professor agreed to write me a letter late November and I i then sent him my resume/transcripts/statement of intent. I also sent him a thank you card over the holidays to express my gratitude. I have not heard back from him since early December, after he agreed to write my letters. I have sent him 1 reminder email on earlier this week as my first application is due tomorrow. He has not responded. I am wondering if it’s proper etiquette to call him? With the rest of my applications being due March 1st I am a little apprehensive that I may run into this issue every time.

I am wondering if it’s proper etiquette to call him? ... Yes, you would do well to call your previous advisor.

FYI, unless they are explicitly required to be there at the same time as your materials, it is often OK if letters arrive after your due date.

Yes, call him. More generally, I see two scenarios,

Your reminders were not received or seen, in this case my next steps would be to call, go directly to his office / after his class and look for him, or see if there is an administrative assistant in the department that can track him down.
Your reminders were seen but he hasn't done it and is avoiding you, in this case, I'd keep politely reminding by phone or e-mail but expect that you may be ignored and then get an e-mail in the 11th hour saying it's done. Sometimes I don't reply to those types of e-mails until I've actually completed the task.

I think it can help to help letter writers by providing a simple document of talking points so that they won't have to spend so much time looking through their documents for details, e.g. "You know me from your Fall 2017 offering of XX class, where I got a grade of XX. I did my final project on XX and you said it was XX (see attached final paper). If possible, I am hoping that your letter could highlight my experience with XXX and my strengths at XXX, which I believe I demonstrated by XXX." This might speed up the process.
Finally, note that some schools do give a bit of leeway on LOR deadlines as this happens often.

If you are able to, you might consider attending office hours if your professor has them openly available before the deadline. In the event that your professor misses this first deadline, I would try to get them to submit the letter to the other schools as soon as possible. It is likely that all of the letters will be basically similar (meaning that all letters should be already written), so an early submission to the other schools should not be an issue.

I suggest that you remind him by SMS first and then after couple of days you could call him if there is no answer. It is ok to call your professor as long as you do it with proper etiquette.

If the deadline is "tomorrow," text now, and call in two hours!