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As a young researcher, what should I follow to keep myself updated in my subject area?
I am a new researcher in immunology and biology. I am unable to keep track of so many papers being published in journals. Do I need to check Conference Proceedings as well? I am not sure what to follow and what to not follow .

although not directly related, this question might be interesting for you: https://academia.stackexchange.com/questions/5585/how-does-one-keep-herself-updated-with-new-research-without-forgetting-older-res/

as is this one: https://academia.stackexchange.com/questions/6105/what-is-the-best-way-of-keeping-track-of-academic-readings-and-persons

maybe this question is of help to you: https://academia.stackexchange.com/q/7615/129015

The role of conference proceedings is field-specific. From what I understand, they don't play any big role in other fields than computer science, engineering, and some subfields that are closely related to these two (e.g., computational chemistry).

I would also suggest having twitter and following people in your field. It is often the case that researchers summarise their work on twitter or mention relevant work/conferences that you may be interested in attending.

It is simply not possible to read every paper published in your field, nor would it be a good use of your time. As @Roland mentioned in the comments - this is something which your supervisor should be able to guide you on.
A few things that I recommend doing are:

Find out who the superstar researchers are in your subfield. Follow them and read the papers they publish - at least those which are relevant to your work (eg. set up alerts on Google scholar/Scopus, follow on twitter, etc). This has the added advantage of helping you learn how to do/write high quality research.
Read papers which cite your papers (you can set alerts in Google scholar, Scopus, etc)- this will give you an idea of how your work fits in with what others are doing.
Talk with collaborators/other students about your research interests (you don't need to give much away and risk being scooped). They could send you papers which they find which may be of interest.
Identify the top journals in your field and keep an eye on papers published there in your areas of interest. You can search for papers published in other journals when you have a specific topic in mind, for example when working on a particular research project.

I'm not in the field of immunology/biology so can't comment on the importance of conference papers.

I like to search publications from certain top-notch research groups at conferences.
I guess it's also good to follow professors on google scholar (you will get notifications on new papers), though I haven't tried much yet.


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Contact chair after VAP position ended to express interest in an RTF position?
I spent 2 years in a Visiting Assistant Professor position, which just "ended" after 4 tenure track positions were filled. I was not considered for any of them. My best guess is that I a) don't have enough research, 2) am older (definitely 40+) so won't be there for long enough.
After the semester, the Chair called to ask if I would like to stay as a Visiting Assistant Professor one more year to teach Statistics, which I am not good at so said no thank you. Yesterday, I accidentally received the minutes from this week's department meeting which state an "request to fill" position was just approved, so they will be "rush hiring" for that this summer. Should I contact the Chair to let him know that I am interested? Or let it go? I am confused as to why I couldn't be considered. I hope it is not an age thing...

Did you apply for the 4 TT positions when they were advertised? What feedback did you get from the hiring committee?

I applied for one of them. I was told I was "old," but not formally of course.

I mean, at 40, you are only good for 25-35 years... Sigh. But, no reason not to apply again I suppose, but keep on looking elsewhere.

I don't understand: You said "no thank you" when asked whether you wanted to stay on, but now you're disappointed that they're looking for a replacement. Can you elaborate? Is it that you are thinking that the position they're trying to fill is anything different from the one that was offered to you (the one for teaching statistics)?

Good question. I am thinking it might be different from the one they offered to me to teach statistics but I am not sure. I am wondering if I should ask or just let it go since there was clearly an effort for me to not be informed of the new position (it was not sent to my college email address, only my gmail address and normally all dept communication goes to both email addresses).

I don't understand where you're coming from and how your thought processes are working. You don't actually know whether the positions are different, but are willing to attribute malice based on the assumption that they might me. You were sent a position announcement by email, but are willing to claim that there was an effort to conceal the position from you. This does not add up.

Regardless of the specifics of your situation, if you don't tell anyone that you're interested in a position, then you will not get it.

I have been on many faculty search committees and helped with hiring of VAPs. I have no access to mind reading abilities to see what's inside the skull of the hiring committees who did not "consider" you (your words.) So let's start with the basics:
Did you apply for the other 4 Tenure Track (TT) positions? If you did not apply, they didn't consider you, even if you made a verbal expression of interest.
If you did apply, then they probably did consider your application and decided against you. At my department, we guarantee VAPs that they'll move to the second round, but that's obviously not your department's policy. For those 4 TT positions, were they for Assistant Professors in your area of research?
You mention that you have little research. VAPs are teaching positions, and it's very difficult to do research with a full-time teaching load. Are the TT positions previously open research-based, i.e. they expect you to get extramural funding? If so, there's your answer. You should apply to teaching-based TT positions. There are many out there.
If they considered and decided against you, it could be the age thing, but I doubt it. Again, I have no insight about what went into their minds, but we hire people in their 40s and 50s all the time.
You also mention that they just got approved a "Request To Fill." Is it for another VAP position, in statistics, the one you rejected? Or for a rush hire of a TT position? To me, it sounds like a rush hire of a VAP. If that's the case, you already rejected that.
To answer your questions:

Should I contact the Chair to let him know that I am interested? Or let it go?

If it is a TT position and your research is within the scope of the advertised positions, yes, apply. A formal application is the only way to know if they want someone like you.
If they don't call you for a second round (short-list/phone interviews), then it's time to move on.

Thanks so much. Great thoughts.

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Is Long 'Last Review Activity' an Indicator that Editors Might Review the Paper Themselves?
I am a novice in the academic publishing process and have recently submitted a paper that is crucial for my upcoming PhD applications. I'm trying to understand the current status of my submission based on the information provided by the journal.
Here's the timeline and status of my paper:
Paper submitted: 10th May 2023
Reviews completed: 1
Review invitations accepted: 1
Review invitations sent: 2+
Last review activity: 16th June 2023
I'm curious about a few aspects:
Does the "Last review activity" date mean that the editor has stopped searching for reviewers for my paper?
Is it common or even possible for editors to review a paper themselves in situations where there might be difficulty finding external reviewers?
Given that it's been over two months since the last activity and the paper is crucial for my academic progression, would it be appropriate to reach out to the journal for an update, or is this waiting period completely normal?
I'm quite new to this, and any insights would be greatly appreciated. Thank you!
edit: By the time 23 August 2023, new activity dropped: Under Review =))

A likely duplicate of the canonical: https://academia.stackexchange.com/questions/55665/what-does-the-typical-workflow-of-a-journal-look-like-how-should-i-interpret-a

This will depend on the journal.   There are journals where it is forbidden for an editor to be a reviewer.   On the other hand, some journals encourage their editors to be a reviewer to speed up the reviewing process.
It does not help to reach out to a journal.  A journal depends on editors and reviewers, who are usually volunteers.  They may take any random amount of time to process a paper.  Some editors/reviewers could go 'missing', even after they have agreed to process/review a paper.

Thank you for the insight. My paper is currently with an Elsevier journal. I'm curious to know if the status of my paper is visible to the editorial board throughout the review process. Essentially, is there a possibility that a paper might go unseen or be forgotten by the editor? Should I not be concerned about the scenario where my paper is "forgotten" by the editor?

Yes.  The status is visible to the EiC and AE.  The EiC monitors the progress of all submitted papers, and may email an AE if there has been inactivity, say after a few months or what is considered outside 'normal' timeframe.  Note, whether this happens depend on whether an EiC is 'active'; e.g., he/she wants to improve the reputation of a journal.

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How to manage thesis professor who is wrong
I am a med school student who is writing her master's thesis. I finished compiling my thesis with all the statistical analyses and wrote them as well. My problem is that today, when talking to the professor who is my advisor, he thought that the statistical part was wrong and asked me to "correct" it in a way that I am sure it is absolutely wrong. Now he wants me to write things his way, even though the deadline is tomorrow. I tried to calmly explain the theory behind it, but I quickly realized that being a doctor, he understands virtually no math or statistics.
My problem is that aside from the tight deadline, I don't want my name to appear on an analysis that is that wrong. I don't even know what I would do during the dissertation since I know for sure that those things are wrong, and I wouldn't be able to defend my thesis. What would you do in my shoes? Should I just write the bulls**t and be over with it? I'm exhausted from all the work I have done, and can't find the force or motivation to work on something I know has 0 value.
About the analysis, if you think I might be the one wrong:

It's a retrospective study where I pooled around 80 patients. Each of them had done an examination 2 times within a certain time period, but with two different machines (once with machine A, once with machine B). We wanted to see if machine B was better when considering around 20 parameters that describe radiation dose and image quality in various ways. So the goal was to see if the same patient had more favourable parameter values when using machine B, when compared to machine A. The professor was very clear in saying that he didn't want to see the overall difference between the two machines: he wants to see how much the average patient benefits when switching from machine A to machine B.
what I did: since we wanted to see the difference within the same patient, I first computed the difference of each parameter when using machine A and machine B, for each patient (so I had all the differences for the 20 parameters). Since patient number was limited, I first assessed if each difference was normally distributed across the patients (using a test called saphiro-wilk). When it was normal, I used the a paired t-test. For parameters that were not normally distributed, I used the Wilcoxon's ranked sign test.
Additionally, for all significant results I computed the Cohen's d to quantify the effect size.
what he wants me to do: compute the mean and standard deviation of each parameter as it is (parameter 1 in machine A, and parameter 1 in machine B). Then run a normal t-test. According to him, the tests that I used are too complicated, no one ever heard of them and no one would understand them, and anyway they make no sense. According to him, a normal t-test preserves the notion that it was the same patient who repeated the test twice. Also, he changed his mind and out of all the 20 parameters he made me look during the study, he suggested that we delete some, and for some others, I should add them together or take the mean (which makes absolutely no sense?!?!?!)
I'm no statistician, I'm just a regular med student that is passionate about science and math. I spent the last month reading books I wasn't familiar with, and writing code on python to implement my analysis. So of course I might be wrong. But the professor's version doesn't sound right to me. Moreover, I feel desperate because I put so much work in it. I just don't feel like going back to coding to implement his wrong ideas (I know it's a simple iteration and I know I can use scipy.stats and that it wouldn't be so long. But I just can't take it anymore)

Sorry for the rant. If you can share your mind I will be grateful. For now, I'm just crying my eyes out on my bed, out of frustration, but maybe I'm not being objective, since I'm running on almost no sleep.
Edit: I finished everything in time and the result is somewhat decent! There still are some parts that refer to the older analysis, but I included it in the appendix (as some of you suggested) and mentioned it, so it should be fine. I can't thank you enough for your kind and constructive support! Having the chance to talk it out and hear some feedback really helped in getting started again.
The tight timeline is not so unusual at my university - at least for my department. But this one was definitely extreme. I addressed the issue this morning when talking to the advisor, hopefully he will take my feedback into account when dealing with future students.

As written, your post is not particularly anonymous anymore. You may want to take that into account.

I suppose it is probably too late to do this if the thesis is due tomorrow (or is this the due date of an initial draft of the thesis?), but would it be possible to visit the statistics or mathematics department of your university (or of a nearby university, if your medical schooling takes place outside of a comprehensive university) and talk about this with an expert in the "no one ever heard of them and no one would understand them" stuff?

If you worry about your graduation, follow the professor's words. If you worry about the quality of your thesis, talk to a statistician and follow their words. You can't have both the quality of your thesis and your graduation at this moment because there is not enough time. So, it's your choice.

I agree with you that a paired t-test is better (although your two-stage procedure is potentially problematic:  (see Rochon et al 2012)).  I agree that a pooled t-test is suboptimal, but I don't think it's horribly wrong - if anything I would guess it would be conservative. The other stuff (deleting/combining parameters) feels a little like snooping though. Rochon et al. 2012. “To Test or Not to Test: Preliminary Assessment of Normality When Comparing Two Independent Samples.” BMC Medical Research Methodology 12 (1): 81. https://doi.org/10.1186/1471-2288-12-81.

Based on @BenBolker's comment, maybe another option may be to take your analysis and put it in an appendix. If the pooled t-test is probably right and specifically if it will be conservative (and if it agrees with your more sophisticated tests) then including it as a handwavy argument that people will feel comfortable in the main text, and then supporting it with what you've done might form a nice balance between the two cases you've considered. Keep in mind to explain what you've done, you need to be understandable by your target audience, so you can convince one way and prove with the other.

Is the deadline tomorrow!? I'm guessing this is an exaggeration made for emphasis? If it really is tomorrow you have no choice but to ignore his demands and submit anyway, then contact the dean or somebody... you obviously cannot make a new analysis one day before handing in the thesis...

Yes the deadline is really tomorrow, at 16:30. I'm overwhelmed. I will try to submit part of my previous work if I don't finish in time (especially the one regarding materials and methods), I have my doubts that anyone will ever attentively read my thesis anyways.

@Elek If your thesis can still be described as a “written draft” and your advisor has only just seen it now, one day before you have to submit, then there's something very seriously wrong with your process at a much deeper level. Your advisor should have seen your drafts months ago; the final 24 hours of thesis writing should be for last-minute polishing and proofreading, not turning a draft into a thesis. I wish you the very best of luck getting your thesis handed in, but I also hope you take something away from this so you don’t end up in a similar problem again later on.

As a heads up, there is a Statistics Stack where you might be interested in asking about the technical content. I see methodological issues in both approaches.

Thank your feedback, I will take it into account if I ever have to do something similar!

Not so sure if I should make this a comment or answer: Not so sure I'll be in a situation of 1-day to go! What can you do. Follow a multi step approach and descriptive statistics. 1) Start with the normal t-test. 2) Proceed with the pair t-test (and the other validators): you may move details to appendix. 3) write up comparison of both and explanatory in as much simple English/Italian as possible: as much lay term for Doctors and audience not versed in technical. Since you're already coding up in Python, you can easily handle same dataset. All the best.

I'm in Italy but things may be different here than where you are, I don't know. In any case in my university the masters thesis has a surprisingly low overall contribution to the final mark, and even if your supervisor thinks there is much to criticise, it may still be enough to pass the thesis and not harm your overall result that much. Check the rules. Also, are you sure about what role your supervisor has in marking your thesis, and how much of a say other committee members have? Ultimately it may not be too big a danger to go with what you have. (Of course I can't guarantee this.)

By the way I'm a statistician but I can't really tell from your overall description who's right and who's wrong. It's a complicated issue and there are a number of aspects I'd need to know that would be to complicated to explain. I'd assume you made a passable effort there and this should not be a reason to fail the thesis even if your supervisor has a point, which may well be. But of course your supervisor may have other ideas - we can't know.

Also consider your supervisor's exact wording and the possibility that even if he criticised the work, it may not mean that it wouldn't pass or even be good as it is. Maybe he was just saying what he thought on the spot and may not even have had the deadline on his radar (or the situation in which he got you)?

I find it ironic that the doctor-in-training says of the advisor, "that being a doctor, he understands virtually no math or statistics." It sounds like a third party who has completed their training in statistics is needed.

I’m not in a position to comment on the specifics of the problem, so will answer the abstract question: what to do if my advisor tells me to do something that I know is incorrect, as a condition for my graduation?
This is tough. I’d say that if you have time, get the opinion of additional experts on the topic (statistics) who may explain why your method makes sense. You should graduate on time, so I do suggest changing things in accordance to what your advisor said, and then follow it up with advice from a domain expert who will hopefully clarify.
As an aside, the more serious issue is that your advisor only saw your results a day before they were due. Research and mentoring are all about good and regular communication, which is where things broke down in my opinion.
Good luck!

Thank you for your advice, I think I will do that!
I was also surprised of the timing because in the previous meetings, he always seemed to agree with me. The problem arose when he saw the written draft. Now I know that he was probably not very invested in our conversations. I'm now back to work.

Another piece of retrospective advice: This is a big reason why thesis and dissertations are normally evaluated by a committee rather than individual advisors. Ideally, your committee should have a few different people with enough knowledge to round out your advisor's expertise.

Thank you for really answering the question and not berating OP "prof is right, you must be wrong".

I have to agree his arguments you've listed don't convince me either. Also, it appears you chose the tools mindfully and, if asked, can explain the decision. In a pinch, that should suffice.
I understand it has already been 24 hours since the question was asked and the deadline has come. You have made your honest best effort and followed scientific integrity. Whatever follows, you will learn from this experience and become a better academic, medic, scientist for it. This one paper, one day of revisions doesn't define you as an academic. As many joke, your thesis hopefully will be your worst paper (because there are many more better and better papers to be produced by you).

Academia is a system which rewards popularity (citations) over truth. It's a system which hasn't fundamentally changed even though it's known that most published findings are false (Ioannidis, 2005). Instead, most of science is still occupied with providing intricate theories from within ivory towers which do not need to work in the real-world at all. The only requirement is that the peers in the field like what you are saying, so you can just echo some important-sounding words around and get many citations. For example, "psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy." (Yarkoni & Westfall, 2017). Similarly, software effort estimation research is mostly based on a few tiny datasets from the 1980-1990s, which are then analyses and re-analysed with trendy data science techniques even though the data itself doesn't reflect the changes in software development since then (Jones, 2021).
As someone who is also close to finishing my PhD, you can be sure that I and many other academics will respect your decision in both cases. Yes, I would prefer that you choose the "right" way. However, I fully respect the fact that you have almost no say in the matter. I wouldn't blame you if you decide to follow your supervisor's orders. I wouldn't even blame your supervisor since he probably has many responsibilities and he probably tries to do the best he can within the constraints that he works with.

Nice diatribe but it doesn't answer the question.

Yes it does answer the underlying question with which I also struggle on a near daily basis. The question is "Should I just write the bulls**t and be over with it?" I answered with: whatever you decide, you can be sure that this is normal and that you will not be disrespected for the decision that you make.

In the end, only the person who asked this question can answer the nuanced question of whether it is possible to push back on the professor.

If someone asks you 'what shall I do?' and you answer with (a) a mostly irrelevant rant and then b) 'I wouldn't blame you if you did either thing', then you haven't answered the question, you've just given a platitude

Could you please give substantive counterarguments instead of the poisoning the well fallacy? You use the words "diatribe", "rant", and "platitude", but offer little substantive counterarguments.

@user438383, if any possible decision is opinion based rather than hard facts based, then stating so is an answer. The highly upvoted answer gives one sided opinion which is fine. But argumentation is not stronger than in this answer. This is not a math question that would have difinitely correct/incorrect answers.

You seem to be omitting quite a lot of what is/was going on, but let's ignore that for now and focus on the question. Tests have the main purpose of showing that something is correct/incorrect (and by how much). You are writing your thesis for doctors - this is the department that will award your degree. Many/most will understand a simple t-test, but fewer have even heard of some of the fancier stuff you describe - let alone understand it.
Putting all of those arguments in support of machine B, while lacking basic ones, is therefore NOT convincing. Such people will read the thesis, see fancy analysis and conclude that if you need all of this fancy analysis to show some benefits, there is actually no benefit to be had in practice and all of this is just smoke and mirrors.
Is it still valuable to have these better tests? Of course, so I believe that you should start the "Analysis" section or whatever it is named with the basic test. IF it shows improvements then great but if not then too bad.
In any case you should continue with results of fancier statistical analysis in the same section. Results of those methods should be in the main part of the thesis, while code could be in appendix; perhaps detailed description/derivation too.
Now, about grouping of those indicators, you can always have a single "goodness" indicator - which would likely be "health" in your case. It is an aggregate of all other indicators you can think of and there can be millions. Obtaining that single number is prone to fudging even if unintentionally. On the other hand, measuring all the separate indicators often leads to stuff like "we get 1% reduction in number of floaters at the cost of 1% higher neck muscle pain" (caricature of the idealized case. Too often only improvements are given). Besides, it is easy to fall in the trap of thinking you got some improvement, only for it to happen by chance (think https://xkcd.com/882/).
I believe you should trust advisor here as he likely knows which indicators matter. Obviously you should NOT simply sweep discarded parameters under the rug, you should mention all the parameters you measured during study, together with what you combined or discarded and most importantly - why. Even if others do not agree with your decisions, at least they will know the potential issues and limitations of your study.

Hmmm looks to me like the conversation was the day before submission so I guess that the draft was not handed in the day before of the conversation, it was probably sent earlier and I expect the advisor had it for some time - you may want to modify your reply

@tom Given the question it doesn't seem advisor had it for a while - it is possible but not plausible imo. If he did, despite prompts to give some feedback, this indeed belongs in the "not you" group in the second sentence of my answer. In any case, getting significant feedback a day before deadline means someone screwed up really bad.

Funny that this answer is sitting at -5 even though it is suggesting OP to do exactly what OP ended up doing. Perfect example of how a bad opening paragraph can destroy an otherwise fine answer!

Yea, the accusatory language in the first para, even though it may be in slight jest, doesn't do the rest of the answer justice, especially since the question itself doesn't mention anything about the draft being given to the advisor the day before deadline, only that they discussed it the day before deadline. Also, it's not an unusual phenomenon that professors don't really read things sent by their students until near a deadline, so the reaction in the first para (with those multiple question marks) feels off.


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How to mitigate harm caused by unfounded allegations?
Someone spread nefarious but completely unfounded ethical allegations against me. That person said he/she was told that I forced a student to plagiarize his/her paper whereas I was the one who strongly advocated for accepting the paper. The paper eventually was rejected because of the oppositions of other reviewers.
The allegations have been heard by many senior people in my field, and have already caused a lot of damage to me, e.g. an honor (given once a year and is highly visible) being retracted, people avoiding me in professional meetings, and who knows what comes next. The person who spread the disinformation is a big shot, is not willing to engage in conversations with me, and is in the so called "leadership" position that can make a lot of decisions. I am very junior and feel powerless.
What should I do?

The post is still quite unclear.  You were a referee?  For the student's paper or the senior person's?  Are there 2 papers that look similar?

I am going to assume that the allegations are completely baseless as you state. If not - I suggest reaching out to the student as a first step to get their version of events, and potentially apologize/take steps to amend the situation. I am gently suggesting looking into the other side because it seems unlikely that a senior person will go spreading unfounded rumors for the fun of it.
Do you have senior folks on your side? Former advisor, mentor? Talk to them and have them intercede on your behalf.
If this has serious repercussions for you, I suggest that you also contact professional organizations that you both belong to (in computer science I’d think of the ACM or IEEE), they have harassment policies that this falls under. You can also reach out to your department head, since this has serious reputational implications for your department as well.
Good luck, this is an unfortunate situation.

"it seems unlikely" - likely even bigger people are known to have lost marbles in the past. It's not wrong to suggest OP does some internal analysis what may have gone wrong, and the description of the case is a bit vague to venture a guess what happened, but a priori, unbelievably bad stuff can happen if people think they are above scrutiny. Not much more help possible without more detail, but that may make OP identifiable. In the worst case, they might consider the legal route. If the academic name is already tainted...

"it seems unlikely that a senior person will go spreading unfounded rumors for the fun of it." When people believe they are defending a victim against mistreatment, often rational thought goes out of the window pretty quick, since any kind of counterargument or skepticism or even listening to what the other side has to say is seen as siding with a bully.

I myself was certainly stunned at how this senior person at that status would go out of their way to damage my career. And this is the unfortunately thing: this person is at the very top of my professional society, and is friends with many others in the society, so even if I file a complaint through them, I am not sure if it will ever go anywhere.

Then I reiterate my suggestion- reach out to your department

The first thing I recommend you do is to document all the problems at issue.  Obtain relevant statements and documentation showing your dealings with this other student, including any evidence that would show that the allegations against you are false.  Obtain relevant statements and documentation of any instance where you know or suspect that false information has been spread about you, and document the negative impact on yourself.  Talk to the people in your field that have heard the allegations and ask them if they can confirm the source of those allegations.  Document, document, document.
There are multiple avenues you could consider pursuing to take action in this matter.  One option would be to make an internal complaint within your university.  Another option would be to make an external complaint to a relevant workplace tribunal.  Another option would be to sue the university and/or professor for defamation.  These options could be used separately or together.  For the latter two actions, you should hire a lawyer to advise you and to review the evidence for your complaint.  If you are lodging an internal complaint at the university then you can expect the university to investigate the matter, but if you are making an external complaint then you will need to rely on your own investigation and evidence.
Since this issue appears to be causing you significant career damage, this is probably a situation where it would be worth spending some money to go and see an employment lawyer to get expert advice.  An employment lawyer will be able to tell you the relevant law in your jurisdiction, tell you all your options for different types of actions, tell you the rules that apply to each, and reivew your evidence to see if it is strong enough to warrant actions of different kinds.

Thanks for the suggestions. The issue is that it's hard to obtain definitive and conclusive evident. I spoke with people who have directly heard the false allegations, but they wouldn't tell me where the source was.

If you were to make an internal complaint then the university would be expected to investigate the facts of the complaint.  Typically this would mean speaking to those employees who have heard the allegations at issue, and they would be expected to respond candidly and completely to the ethics officers.  Thus, even if they won't talk to you, they probably would not stonewall an investigtaion within the university.


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Referee for two papers by the same author: can I let them know?
I am a referee for two papers by the same author: can I acknowledge in the second report (the first was almost ready when I was invited to referee the second one) my report to the first paper?
To give more details: I am suggesting acceptance to both papers, but I think the second one reexplains too much of what is developed in the first paper (with proper citation, but to a slightly different version in preprint), and I want to be able to tell the author they should simply cite the other work and move on to the new results. Furthermore, both papers make some odd choices about presentation, and it would be easier for me to simply point to the other report than to explain again in great detail why e.g. an alternative definition of some concept would make the work much easier to follow.
This is a single-blind peer review, so I know who the author is, but they don't know who I am. I don't see how doing this could be a problem (both in terms of ethics and etiquette), as they won't be able to tell who I am just from knowing I am a referee for both papers, but it seemed wiser to me to ask, rather than assume it is perfectly OK.
Edit: the papers were subjected to different journals, and are being handled by different editors. So it was just by coincidence that both ended up with me as a referee; but not a big coincidence because the field is rather small...
Edit 2: I have accepted Buffy's answer because of the general advice on how to proceed: contact the editors, and keep in mind that the first paper may not get accepted and that the author may get confused. But, in my specific case, I am following more or less quarague's suggestions: I am writing the two reports more or less independently, and referencing the preprint when talking about the first paper on the second report, but I will not make great efforts to hide that the two reports have been written by the same person (I will not explicitly mention it, but it will be clear from the similarities between the comments on both reports).

At the point of submitting the authors couldn't know which papers would be accepted and what they would be asked to change in each of them. As chances are there are also other reviewers, and anyway the editor has the last word, you don't know that either, despite your recommendations. So you can't assume when making recommendations on one paper whether (and when) the other paper will be published as you have seen it. I think they can't rely in one paper on explanations in the other one that is not yet accepted

Are the papers publicly available at ArXiv or similar? Then you could refer to these.

@ChristianHennig But the first paper is available as a preprint: I just don't think it is reasonable to spend most of a new paper copying a previous paper just because you are not sure if the old one is getting published... I mean, that is why preprint repositories exist.

@usr1234567 Yes, it is available on arXiv: I think the best way forward is probably to just refer to the preprint, but not make any explicit mentions to the other report in the new one.

I suggest that you let the editor(s) know of your concerns before you put such things into the report itself. Take their advice.
Indicating you have seen the other paper might confuse the authors about the standards of the review process.
I've assumed this is for the same journal and likely the same editor. It is a little trickier if they are different journals. If the journals are different then you can ask both editors, assuming you are comfortable with that. Otherwise, I wouldn't include such things in either report. You can, of course, suggest changes.
Note that it is possible that one or the other of the papers won't be published, in spite of your recommendation.

They are actually for different journals, and different editors...

I think I will probably follow the part of this advice pertaining to the case where I do not contact the editors: meaning I will write both reports without explicitly mentioning each other (but I will also not go out of my way to pretend the two reports have been written by the same referee), and if I have to refer to the older paper, I will cite its preprint. I will probably accept this answer later today or tomorrow...

My short advice would be to make the reviews independent, as far as feasible. While you say:

I don't see how doing this could be a problem (both in terms of ethics and etiquette)

I think this is an extremely unusual situation (as you yourself acknowledge). An author who recognizes the situation might feel that both papers' acceptances or rejections are riding in tandem on them getting both papers "right", especially if they are inexperienced.
While that stress is probably not too harmful, I can't see how it would be helpful or would result in better papers, so I would lean towards "first do no harm" in this situation and avoid unnecessarily stressing out the authors.
In terms of the situational specifics:

I want to be able to tell the author they should simply cite the other work and move on to the new results

Then you should just say that, based on the content of the preprint. If this issue arises in the final version of paper A but not the preprint, then as a reviewer I personally would just let it slide, since (1) under almost all circumstances these papers would be independently reviewed, and (2) it doesn't do significant harm to science to have two extremely closely written papers happen to repeat significant parts of each others' content.

both papers make some odd choices about presentation, and it would be easier for me to simply point to the other report

You could just cut and paste from one report to the other. If the presentations are a problem then they're a problem in each paper independent of the other paper's content -- so just comment on the problematic presentation as if that's the one paper you've read.

I agree with almost everything written in this answer, with one exception: "(2) it doesn't do significant harm to science to have two extremely closely written papers happen to repeat significant parts of each others' content." I think the papers become much less clear in this case...

If you mean copy and paste rather than cut and paste: that would clearly reveal that it's the same reviewer, contradicting the "tandem" argument above. So if feasible, write those thoughts in different words in the other review; this makes sense particularly if those parts of the papers are not identical and thus optimal reviews are not identical. Instead of "explain again in great detail why e.g. an alternative definition of some concept would make the work much easier to follow" in both reviews, explain it understandably for the editor but in less detail in the review that you'll send later.

I see two issues you want to address here. First, the second paper rehashes a lot of what is already written in the first paper. As the first paper is available as a preprint and referenced in the second paper this issue is not related to you also reviewing the first paper. Your review for the second paper can simply critisize that aspect.
The other issue is that you want to suggest using slightly different definitions or naming conventions. This issue is present in both papers. I think the best solution here is to just copy a version of your argument into both reviews and adjust them as needed. That way each review can completely stand on its own but an astute reader would notice they are probably written by the same person. That is fine, there is no reason to hide that.


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How should I handle an abusive research mentor as an undergrad?
The professor I do research with often puts me down for making mistakes and frequently calls me an idiot. I don't know the appropriate way to respond. I cannot just leave, since I still want to go to grad school. But this is severely affecting my mental health, and I have been suicidal recently as a result. I put a lot of effort into trying to be positive and friendly around him, but I'm not sure this has been particularly effective.
The research I do with him is good in a technical sense; we have some solid results and have published at very good conferences together. He has agreed to write me a good letter of recommendation (but actually hasn't written it yet) and I believe him if only because he seems to care strongly about his reputation among his peers and writing me a poor letter would reflect badly on him. I do not know what to do.

don't take the professor's comment to heart.  If you can, just ignore the 'emotions' and focus on constructive comments.  Passionate people tend to be passionate about every little thing.  2) remind your professor you are still a student and learning the ropes.  Some times we forget that, especially when we are results oriented, and 3) be prepared and pay attention to details.  I for example get ticked off if students keep on repeating the same mistakes again and again.

I cannot just leave, since I still want to go to grad school. — Yes, you can! You can just leave, and you can still go to grad school.

I especially like @Prof.SantaClaus suggestion #2 here. I once read that it was especially important for new employees to pepper any questions with phrases like, "As you know, this is my first X experience..." to remind people that they were still new after their first month. Apparently it is very easy for supervisors to forget these things and categorize you as "trained" after a ridiculously short time.

How close are you to graduation? You have basically three paths here:

Stop working with your advisor. Depending on how bad it has gotten, escalating levels of this solution could be trying to get another member of your advisor's group to take over working with you so you have to see him less, to find a new advisor to continue your current work, to take a semester of mental health leave and start over on a new project afterwards, or even to drop out.
Try to make your advisor behave better. You know your advisor best and can better judge what he would be likely to respond to. Maybe if you just told him "Please don't call me an idiot," he would stop. Maybe if you could get the ear of a sympathetic member of your faculty, they could take your advisor aside and tell them to be more gentle. If your university offers counseling, maybe a counselor could help you raise the subject with him and negotiate some rules for how to work together. Maybe you can raise a formal complaint somewhere (dean, ombudsman?). (The last one will probably torpedo your chances of good recommendation letters, but might let you graduate without having to take more abuse.)
Tough it out. If it's only a few more weeks, maybe deciding that you choose to take it just a little bit longer because you think that the rewards (graduation, recommendation letters) are worth it helps you get through it. This also needs a good support network. Talking things through with a therapist or counselor can help distance yourself emotionally from your advisor's comments. Let your friends console you. Seek affirmation from other places: are there any other activities where you are doing well? Classes that you are excelling at? A hobby that produces things you can be proud of?

No matter which option you ultimately choose, I encourage you to at least informally talk to a university counselor and whoever handles complaints to be aware of all your options. For each of the options, what process exists? What are the upsides/downsides? Make a list, and then make a deliberate decision. Sometimes, even just knowing that you could leave and do something else but are actively choosing to stay in your current situation for now because you feel the recommendation letters are worth it makes all the difference.

If you're in the U.S., get a letter from a doctor or mental health professional and take it to (a) the Students with Disabilities office on campus, and (b) an administrator in your department.  The letter should state your diagnosis (diagnoses), mention the suicide attempt and/or ideation, and briefly explain the connection between disrespectful treatment and your stability.  Bring along a list of specific changes you need to support your mental health.
Switch advisors as soon as you can.  Ask a department administrator for help with this.
Find a supportive therapist or therapy group or support group.
Later on in your life, when you are more stable, you may wish to try to improve a troubled working relationship.  For now, though -- distance yourself from this guy, and focus on learning as much as you can from the supportive folks.

I'm not from the US so apologies if I'm completely off-base, but wouldn't the "department graduate advisor" only handle graduate students, not undergrads?

@nengel - You're right.  I forgot the OP is an undergrad.  I'll fix it.

@aparente001: The first "administrator" should be the undergraduate's advisor or the director of undergraduate studies (or equivalent office)!

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make sense of index terms in dl.acm.org website
I am a graduate student. I plan to submit my master project this year.
While browsing relevant papers in my research field, I wonder how making sense of index terms on the site dl.acm.org
For an example, consider this link: dl.acm.org.

What is your question? The tags are author supplied when the paper is submitted. The system is hierarchical. The tags are used to help readers find relevant papers, much as the tags used on this site. They are also used to help in finding editors/reviewers as appropriate. See this: https://dl.acm.org/ccs

Did you do Google search on "ACM index terms"?

Thank you so much @Buffy.

@scaaahu I did, but there but my query was not relevant as your I guessed. thanks guys.

Similar to what is done at other professional societies that publish journals, the ACM has a classification system for submitted papers. Authors classify their own submissions. The system is hierarchical to provide a bit of granularity.
Potential readers use the assigned tags to find articles relevant to their own interests, and the editors use them to assign area editors and reviewers.
The tags used on this site are a bit similar in terms of finding questions related to a readers interest.


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How do I explain my poor grade to a grad school committee?
I'm an Indian student in my 2nd year and I have a subpar(6/10) grade, but that's just because of the school I'm in currently.
I've been a straight A student before university all my life. I'm pretty sure that it's  this school which is super competitive and likes giving extra-hard tests and strict grading which leads me to have a poorer grade, despite knowing/understanding the material. And I'm not the only student who's been disadvantaged here in this manner.
I've interacted with several other schools/students here, or outside India, for example  and I'm pretty sure I'd have a higher grade if I were at one of those schools.
I understand that being challenged is beneficial and the post might just come across as one from a poor student who likes to whine, but my thoughts are genuine. Other factors that contributed to a poor grade are my ADHD and the utter lack of course flexibility.
For clarification, I'm a CS student and I'm talking admissions committees outside of my country(admission to an engineering masters program here takes place differently through tests) - perhaps in the USA/Canada. Are these valid reasons I could get through to them? How?

I've been a straight A student before university all my life.

Great!  You're finally among your peers, who have also been straight-A students their entire life before university.  Finally, schoolwork isn't just a trivial boring exercise in pencil-pushing!  Finally, you can actually test your limits!  Finally, an opportunity to be challenged, to be confused and frustrated---in short, to actually learn something!
By definition, only a small fraction of your class can be significantly above average.  Fortunately, graduate admissions committee know this.  We don't just look at your grades; we also look at the institution that gives them.  A perfect GPA from a school that typically admits weaker students is worth significantly less than a merely-pretty-good GPA from a school known for admitting strong students.  A 3.2 from MIT or Harvey Mudd is worth more than a 4.0 from the University of Southern North Dakota at Hoople.
Do not under any circumstances make excuses for your grades in your graduate school application.  Write about your accomplishments.  Write about your expertise.  Write about the challenges that you overcame.  Present the evidence that you will thrive in your target graduate program(s).  Sell yourself as a mature, hard-working student, ready to hit the ground running.  Do. Not. Make. Excuses.  No matter how unfairly you believe you have been treated, admissions committees will read your complaints as whining, and they will admit some other student with a mediocre GPA from a hyper-competitive school instead of you.

Sorry, but I had to check! --- University of Southern North Dakota at Hoople And good point about the whining interpretation, which I personally got from just reading the question itself.

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Selecting the Theory Underpinning the Research Project
Sorry about the breadth and complexity of this question. I realise that for most people this question irrelevant as most projects or PhDs are predetermined and that theories and methodologies for each discipline or topic are quite limited. However, for me that is not the case. My research topic is rather broad, and I was keen to explore the topic from different angles before deciding on a specific angle. When the angle was decided, it was not clear which theory to use and whether the project should be one of theory development or theory testing. I have since decided which theory to use but I would like to apply more intellectually rigour to my project.
Anyways, my broad question is directed to people and researchers who have experience or had to decide which theories to use for their project or PhD. What influenced your decision-making and what factors were relevant? I could not find any books or resources that helped me go through my consideration process, so I apologise for this question.
More specifically my topic is broadly about new migrants. I was keen to explore whether migrants who are new to Australia found or felt there are educational differences in the way they are taught in the workplace. Obviously, learning theories are relevant, acculturation theories less so, etc.

What does your advisor say?

They are supportive of the theory I have selected. I am still in the process of discussing it.

It is a bit out of their area of expertise and I have tried canvas across disciplines but I am not feeling totally confident and satisfied.

It is difficult to answer your question as this is one of the main difficulties of doing theoretical research. For me, it has always been a mixture of what looks more promising and what looks more interesting/"fun" to work with.

Good point @wimi. I come from a science and more positivist training so the "mixture" is less appreciated. Fun and interesting can be important points. Thanks wimi

Whenever you have a problem you can't solve, solve any part of it, then take another look at the problem and see if the partial answer has helped. (I can't find the exact source of this formula, but I think it's R.A. Heinlein.)
First, what sort of methodology are you considering? Interviews? Surveys? Online or in person? What sort of statistics are you contemplating, if any? How will you assure you have something representative rather than the result of a small number of people with unusual opinions? How will you select your subjects? Is there any method to deal with self-selection? For example, people who are unusually happy or unusually unhappy are possibly more likely to want to talk about their experiences.
You write of differences. How will you determine the situation before they migrated in order to observe the difference? Suppose, for example, that you had a significant number of people with very little work experience outside of Australia, maybe because they migrated in the age range of 10 to 18, and so didn't have jobs previously. How would you discern "coming of age" factors from "changing culture" factors? Possibly you need to do some research on the situation in the places the migrants migrated from. Possibly you need to compare to the perception of experiences of non-migrants in the same age range. Possibly you need to control for financial status, education status, etc.
How will you detect the just so story in your work? If you do detect it, what will you do about it?
There are a bunch more such questions you could ask yourself. I'm a humble safety analyst, so I know very little about sociological studies. I do know something about how to avoid fooling yourself.
Once you have some notion of that, then you can look at theories that you could possibly test using such methods. Then you can refine your methods to target specific hypotheses. Be sure that the research you propose has a reasonable expectation of actually testing your hypotheses. Also, have some thought as to whether there might be alternate hypotheses that have similar explanatory power.
And after you've done all that then stand back and critically examine the whole thing. Maybe there are other methods that would be a similar amount of work (or even less work), but that would test these hypotheses more accurately. Maybe there is already a block of research that tests them. Maybe there is some reason to think some of your hypotheses are wildly wrong before you even start.

Thanks puppetsock. I appreciate the perspectives and it is helpful to think about those different ways.


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How do applicants for an NSF fellowship come up with a research plan for the research statement part of the applications
I understand reviewing literature is an important part of completing the application. Do most applicants develop a plan independently solely based off of the articles they read or do faculty advisors usually give students a specific question to write about? How feasible are the research plans written by say a first year grad student who is new to their field? Is the intent of this part of the application just to demonstrate that the applicant is familiar with their field and can formulate a research plan or is it meant to be an actual plan of action for their grad student career.
The context of my question is I am considering whether to apply to the nsf fellowship this year or next year. I would say I have done enough literature review to identify holes in the literature that I could possibly propose a research plan for, but I am not sure if it would be something I would actually study or not because there are many things up in the air in my lab related to available facilities/research direction, etc.

As academic staff who has presented on the GRFP application process and helped people with their documents, I wonder - have you talked to your PI/advisor or undergraduate mentor/advisor about the feasibility of your proposed project? Follow Buffy on that. Also look into what writing support services are available, because they may be able to help you pitch your writing to a more general (field-specific) audience. Probably half of what I do with individual applicants is point out how they can make their "Intellectual Merit" or "Broader Impact" more specific, compelling, or clear.

Having reviewed GRFP applications,  and gone through the NSF orientation sessions, I'd say it depends very much on the level of the applicant, but generally, literature reviews are not anywhere near as important as you thought they were.
This process only vaguely resembles a research grant review, primarily because there is ZERO effort to match reviewers' expertise to those of the applicants.  Broadly, electrical engineers will review applications of electrical engineers, and biologists will review applications from biologists, but that's as fine-grained as the process will guarantee.  This means that you can't display fundamental gaps in knowledge that any practitioner in your broad field should know, but in all likelihood, nobody is going to go check your sources, or know much about the very special second messenger system that impacts a new subtype of cation channel that 2 people in the world study.
As to where the research plan comes from, that's going to vary incredibly by the level of the applicant.  A student about to start a program, who hasn't even set foot on their campus yet, is very different from a student that's been placed in a PI's lab for a year, and both can get funded!  For the young student, reviewers are looking for your ability to put together a cogent hypothesis-driven study and communicate it well (and not many reviewers would believe this is the study you will actually do your graduate work in) along with a somewhat viable path to get yourself established with the type of resources you would need to actually do similar research, and somewhat more than that for an experienced student.  My recollection (though I could be wrong) is that there is some earmarking of awards to the four levels  of application status, and that young applicants will get a competitive amount of awards, even though the experienced students clearly have more polished packages.
Reviewers are trained to assess you in two areas; "Intellectual Merit" and "Broader Impact".  Both are rolled together to make a holistic assessment of your application package.  Reviewers are looking for evidence that 1) you've got the intellectual goods to thrive in science, 2) you've made opportunities for yourself and have taken advantage of them, and 2) that you see areas where you can bring your skills to the larger community.  I can't say that you'll be successful if you can demonstrate success in these areas, but I can say that you won't if you can't.  Using your literature would be a part of this, but I'd say that if you don't make any mistakes in your use of the literature, you won't be hurting your application.
As to whether you should apply this year or wait until next year, that's a tough call.  The risk with the "make believe" type of applications, where you aren't even affiliated with a lab yet, is that one reviewer might not buy that the path you've outlined is viable, and considers this to important to recommend funding (I try hard not to do this, myself, though naivete can certainly be a strike against intellectual merit). This will kill your application.  I don't believe you are excluded from applying a second time, though, so the risk of application this round seems minimal.  You should verify eligibility for a second submission on your own, though (i.e., don't take my word for it).

There are a wide variety of solutions for this, but, mainly, the advisor/PI is a good source for advice and even for a specific problem.
Some people come up with their own ideas independent of an advisor, though I think that this isn't the most common path. But at least, if you are able to come up with what you think is a plan, then your advisor should be able to give you good advice about its potential and any caveats that there might be.
A new researcher will generally (not always) find it difficult to measure the potential outcome of a research plan. Some ideas are too big to study as a student as the field isn't "ripe" for their solution and it would be unlikely to result in success. Others are too small to be worth studying as the results might be seen as trivial. Ideally you want a "just right" sort of problem and the advisor may be a better judge of that at the beginning than the student is capable of. So, get advice.
My own study was instructive. I worked under a really great mathematician on three problems. One of them was so easy that I could generate a new theorem and its proof every day or so. Kind of fun, but lacking substance. Another was so hard that I couldn't see any crack in the problem that might be exploited into a solution. The third problem was "just right": hard and significant, but do-able. It resulted in a really great dissertation, but I couldn't have predicted that at the beginning of the exploration.
IMO, a literature review probably isn't enough, though you indicate you have found some holes that might be filled. Write up a couple of those and ask your advisor what s/he thinks about them as potential problems. Advice is good. Forgoing it is just thrashing in the dark.


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Supervisor is not reading unpublished manuscript
I created a rather controversial review of empirical data for my PhD thesis. Initially, my supervisor was very discouraging regarding this review, to the point of threatening me to end my PhD.
Believing the notions contained in the work are very important, I continued the work. After the supervisor read the script, providing minor comments on clarity, they were very pleased and even proud. From this work from my thesis I have made a paper. I sent it to all potential co-authors months ago, and I do not get any response from them. The supervisor, on the other hand, says that unless I complete other work, they will not read it (yes, the supervisor tends to change mind quickly and not have much memory of previous attitudes, partly due to being far too overworked). They also stated that they would contact any editor if submitted saying that the work was conducted under themselves and that the editor should not publish it.
Quite frankly, I am not too keen on receiving comments from the supervisor as I believe they will be most likely political in nature (i.e., who not to offend in the field etc., rather than scientific), further delay the paper (which i believe should be published ASAP), and possibly detract from its content.
I would like to move on alone, perhaps seeking comments from other scientists, but it seems I am in a dangerous situation and that the supervisor's higher stature and volatile attitudes may provoke significant problems for me in my career (also considering I do not have many contacts willing to support me with data etc.,).
My questions are

Do you think that, worst comes to worst, I can publish the paper on my own?
Tactically speaking, how bad do you think things have to get before I should consider such an option?

The sentence "After the supervisor read the script, providing minor comments on clarity, they were very pleased and even proud" seems inconsistent with the rest. Can you clarify? Did they go from negative to positive and back to negative? Why co-authors? Why "political". Needs clarification.

Hello Buffy, thank you for the comment. I have made minor modifications to the question. Yes, they did go from negative to positive to negative. Although confusing, they tend to do this quite a bit, and, tendentially, not remember or recognise much the inconsistencies. The "political" refers to the reviewing of manuscripts primarily thinking about what other people may think/feel rather than the rigorousness of the scientific content.  Co-authors were other people that may participate due to certain expertise which I don't have. I also have the issue of how to fund such a publication.

What is the question?

Sorry, Thank you for the comment. Am new to this. Explicit questions added.

Single-author papers based on (parts of) the research done for PhD thesis are common. This is your research you did for your PhD, and as such, it is supposed to be your independent work done under a supervision (guidance) of your advisor. Some people include their supervisor in the list of authors, but this is not strictly speaking necessary.

From this work from my thesis I have made a paper. I sent it to all potential co-authors months ago, and I do not get any response from them.

This sounds a bit strange to me. If you wrote a paper, you are the single author. What are these other "co-authors" and why you sent your paper to them rather than a journal? Normally, co-authors are collaborators who work with you before and during the research is done, and this joint work results in a joint paper. You are doing it somewhat backwards --- are you sure you understand how the academic publishing process works? Take a look at e.g. Cambridge Guidelines on Authorship.

Thank you so much for your answer.
Indeed it is strange, however, In our other papers I have been "asked" to include lots of people that I have not met due to their funding, dataset, data curation, clinical aspects etc.,
In this case, I was hoping the "potential" co-authors would be able to give an intellectual contribution via their specific expertise.

‘ Single-author papers based on (parts of) the research done for PhD thesis are common’ maybe specify a certain field because it’s absolutely not common in my field.

Neuroscience is the field.
Thank you all for your help. It is much appreciated. I have decided I will try to publish it on my own. Wish me luck! :)


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Is undergraduate research in applied statistics still relevant experience for a math PhD program?
I'm an applied math and data science student currently doing undergrad research in statistics. I don't want to dox myself, but it falls under optimization research. It's a good mix between math and data science, but I want to understand my chances when applying to graduate school in a couple of years. I'm talking in reference to competitive math programs, such as HYPSM and CMU (I have specific schools in mind).
This isn't general grad school app advice, although I'd love to hear it out as well! I've also seen that similar questions have been asked elsewhere on here, but I'm not sure of the mathematics admissions climate in particular. Specifically, I'm wondering how important it is to do research directly within math, even if your research interests are an "indirect" branch of math.
Edits: Any added info based on feedback from y'all, I'll add here.
Geographically, I'm from the US. I go to a southern R1.

Where (geographically) are you?  American and European schools, for example, have different expectations regarding what a students with a bachelors degree should know and be able to do, and what an incoming graduate student should know and be able to do.  Without having more specifics, it is hard to answer the question.

@XanderHenderson, just updated the post with more information!

To me, whether it's beneficial for your PHD applications depends on the level you're working at. To make an analogy, it would be a little like asking if your work as a first year dancer could get you into the Julliard School of Dance.
Maybe. Can you literally and metaphorically dance with the other applicants and not miss many steps? PHD programs worth your time will focus on RESEARCH. Grades and stuff matter, but RESEARCH, you need to show that you can do research at the graduate level that suggests you'll be successful in those programs at those schools.
In other words, if you could make whatever you're doing into a solo publication, then I'd say it helps you. It shows that you can compete with other me experienced applicants at a younger (I think) age.
Edit: my field is in econometrics and statistics (even though my major is public policy). Some might call me a data scientist. If you want more of my thoughts, email me. But, these are my general remarks. It's all about how competitive you are, and if the work you're doing right now is a good fit for these programs.

In short, the answer is yes: your work in applied statistics is relevant.
First, applied statistics is, in a way, still a branch of mathematics.
Second, the people from Admissions will want to make sure that an applicant is mature and knows exactly what (s)he wants. You thus will get extra points by emphasising in your cover letter that you have some experience, and this experience has convinced you that your choice is right and you absolutely must become a mathematician.
Last, and by no means least, the Admissions people will be interested not so much in the particular area of your experience but in your ability to conduct research, to achieve results, to complete the projects you started. So, if your work in applied statistics leads to a publication -- that will look very good in the eyes of both the Admissions and your prospective advisor.

In the US, it is natural to switch fields when applying for a doctorate with only a bachelors. You will need to take some advanced math courses in the program, but so do others. Your background in analysis, is probably strong, but probably less in the other required areas.
Note  that not all undergraduates have an opportunity to do research and even fewer to do serious research. That is unlikely to be a limiting factor.
OTOH, any positive thing you do is "relevant" in some sense, though applied stats and pure math are pretty far apart.
However, limiting applications to only the very top schools will be unlikely to result in success. You will be competing in a large pool with others better prepared to start. Perhaps there is something unique about you that will overcome that. And many in that pool will have some research experience.
I suggest that in addition to the schools you are considering you cast a much wider net among R1 universities. Perhaps you will be successful with a narrow search, but it your chances of finding a suitable place expand greatly if you open up to most of the R1s, down to at least #50. You will find strong programs in math at most of them. Not all highly qualified faculty wind up at the top four or so schools.

Thank you so much for the feedback! I described the schools as HPYSM misleadingly, because I don't want to apply to most of those schools anyways! I like the research at a couple of them, just didn't want to be overly specific. I feel the same way about R1 professors even if theyre not at the top four, particularly from experience with them as mentors/professors. I do have a couple more questions:

What required areas should I focus on?
What did you mean by "better prepared to start"? Would applied statistics research not be within the scope of an applied mathematics PhD program?

Those others will have had the upper division math courses that you may not. Focus on those. Algebra and Topology, perhaps. Applied stats is closer to applied math than to pure math, of course.


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On proving results in a completely different field
I am a postdoc in a field of mathematics A. Recently, I began talking with a professor that works in field B that is completely unrelated to A. He had a conjecture, and after a month or so, I was able to prove it. It is possible that this will be an important paper (although I am not sure if personal conjectures are as important as "known" or "famous" conjectures).
Will this affect my faculty job applications at all, assuming that I will be applying for positions in my original mathematical field? Will the committee just look upon it as a curiosity and then move on? Or does it provide some evidence of "well roundedness" that might help overall?

It will look good, especially if your paper is published by the time you apply for jobs.

If my limited experience is any guide, it'll get counted as "Good paper in a good journal" and nothing more unless someone in the target dept. is specifically interested in the topic. But that's every paper. A paper in a month of work can hardly be a bad thing.

@matilda Does it matter? It's not like you can trade it. You'll apply with the papers you've published in your CV, not with hypothetical other papers.

@BryanKrause- I just meant to ask if I should focus on solving problems in my own field, or keep myself open to solving any problem that I might have a chance of solving, even if they lie outside my field. I don't want to appear "unfocused", for example.

"field B that is completely unrelated to A" Just as a side note: Are there indeed mathematical fields that are completely unrelated?

@JochenGlueck can unrelated fields be proven to exist / not exist?

@JochenGlueck: Try to find direct connections between, say, Model Theory (in logic) and hyperbolic PDEs. Of course, you can always go back from both to the foundations (set theory, etc), but this is very indirect.

@MoisheKohan: Ultraproducts from model theory were adapted to Banach space theory (in the 60s I think) and subsequently used in the spectral theory of positive linear operators and operator semigroups. Such results are closely tied to the long-term behaviour of various types of transport equations, which are hyperbolic PDEs and variations thereof. (They are also related to further types of PDEs, but I tried to stick to the specific theory you mentioned).

@JochenGlueck: Do you know of any PDE paper that actually refers to these results? As for ultraproducts themselves, this is a tool that was invented by people in the model theory.

@MoisheKohan: For instance in Section 4 of this paper Glenn Webb studies a transport equations from mathematical biology. The long-term behaviour of the model is analyzed by means of Proposition 2.5, which in turn relies crucially on a cyclicity result for the peripheral spectrum of generators of positive semigroups which is due to Greiner (Theorem 2.4 here, unfortunately in German) and which uses (a version of) ultrapowers. [...]

[...] This spectral result by Greiner is central for the Perron-Frobenius theory of positive operator semigroups, which is very commonly used to study linear transport processes. I could give various further references, but it would take unreasonably much time to check for each of them whether they indeed use precisely that result of Greiner or another result from the same theory that was proved by different methods.

@MoisheKohan: To give just one more example, this paper studies a PDE (but a parabolic one rather than a hyperbolic one) and one can trace back the analysis of the long-term behaviour to Greiner's result (via the aforementioned paper of Webb). A good source to look for more examples is certainly the further work by Webb or the work of Thieme and of Mokhtar-Kharroubi.

Cherish it, Matilda! Such things happen in science all the time. One interdisciplinary area I have kept an eye on is the interplay of telecommunications and math. What happens frequently is that the engineers run into a practical problem, and think of some solution. When the problem is described to math people, they take over, and use their tools to improve upon the solutions (and/or the range of parameters where solutions exist). If you are lucky to be the right contact, just cherish it. It won't compromise your pure mathematician's soul to apply math like this occasionally.

Congratulations, this sounds very cool and I love stories like yours. The simple, literal-minded answer is that any good paper proving someone's conjecture will surely help your career.
However, I think what you are asking about is less about whether this paper will help you, and more about whether this paper will help you more or less than some other paper you could be writing if you invested the same amount of time working on your "original" field. In other words, this is a question about opportunity costs.
It's hard to give a definite answer, since you can find successful mathematicians who spend their whole careers studying a relatively well-defined topic, and you can find successful mathematicians who move around a lot and work on lots of different topics. And you can also find not-very-successful mathematicians of both of those categories.
Personally I think it's healthy to always be actively aspiring to learn more about other areas. This does take effort, which could potentially be more efficiently invested in obtaining new results in your "current" area, but in the longer term it yields dividends and helps you grow and mature as a mathematician and ultimately produce better research. However, one must be careful not to take this mindset to an unhealthy extreme - certainly some focus is required so you don't spread yourself too thin and are able to consistently publish good work, particularly in pure math where many mainstream areas of research require an intense level of focus and concentration to make progress on.
Another thing to consider is that how focused to be with your research may be a personal inclination tied to your personality, and that trying to fight your personal inclination is not necessarily a good idea, and may even be futile. In other words, even if perhaps there is a theoretically optimal level of focus one should aim for in order to maximize career success, you may discover that this is not a level of focus you are interested in, or are even capable of pursuing. (I speak from personal experience here.)
I'll end with a philosophical — almost mystical — thought: I believe that each mathematician has talents that are well-suited for some kinds of mathematics research, and less suited to other kinds. if you made an important discovery in field B even though your "official" research is in field A, one way of thinking about what's happening is that the universe is sending you a signal that you are the kind of person who can do really good work in field B, or even that you are more generally the kind of person who can do really good work in new fields that are far away from those you already mastered. I believe you should pay attention to such signals - they tell you something important about yourself, and by listening to them you can improve your intuition about what fields to pursue research in, what problems to work on etc. In the long run, this will lead you to become the best mathematician that you can be, and to maximal personal fulfillment. And hopefully to career success as well!

I think it is a definite positive in more ways than one. Any good published paper is good on the CV, but in math, being known for some breadth of insight is an excellent qualification.
And, beyond the image itself, is the essence that insight in more than one subfield can bring insight to either and, beyond.
Assuming that you are published in a reputable journal, I can see no downside. Quite the contrary, being too narrow can be a detriment and leaves some doors closed that might be opened.
Paul Erdős, for example, was open to ideas in many fields. Nobody much thought of him as "unfocused".

Ha, no, actually everyone thinks that Erdos is sort of the prototype of the unfocused mathematician. He never stuck with a project, usually didn't do the writing and sorting out the details, and moved on to the next exciting thing. What set him apart is that he was good enough to get away with it :-)


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On cold emailing professors about your research question
I am a postdoc in Mathematics in the West.
I am currently stuck on a research problem, and the things that I am stuck on lie far outside my domain. Hence, I have tried to cold email professors who work in that field about thoughts on my problem, and if they have suggestions.
They don't often respond. What is the etiquette on following up with them? Can I send them a "reminder" email? Or should I just assume that they are not interested in responding?
Also, what is the best way to interest professors in your research? I do believe that my question is important, for instance. Are there certain things you can say in your email that might interest them more?

If you are a postdoc, you should be able to get in touch with people at your (local) university, do that first, even if you think the best expert on topic X is the guy on the other side of the world that can be reached by email only, your question may be answered by "regular" practitioner of topic X sitting next building to you.

and, reg. "They don't often respond", maybe you did not notice it, but you are soooooo lucky for the few that respond :D !

Did you try to ask your math question at Mathoverflow?

@MoisheKohan- Yes, I have

"Yes, I have" Would you bother to provide the link? :-)

@fedja- Thank you for your comment. I was hoping to catch your attention with my questions. They are- https://mathoverflow.net/questions/461660/on-a-deceptively-tricky-calculus-problem, https://mathoverflow.net/questions/461857/find-a-function-f-geq-0-such-that-e-tx-partial-x-partial-x2-f-is-no

@fedja- This is the main question that I am trying to answer- https://mathoverflow.net/questions/462606/if-the-operators-b-i-satisfy-an-inequality-prove-that-b-1-dotsb-b-n-al
Thank you

One option is to try asking your questions on the Stack Exchange forum for your subject.

Cold emails are easy to ignore and many people do so fairly automatically. They have their own research and other duties and thinking about someone else's issues takes effort and a change of focus. People do it when they know one another and when their focus is already elsewhere (think coffee lounge informal conversation).
But it you have already made contact with someone it is more likely you get an answer. That is one of the chief features of professional meetings: meeting people and informal research discussions.
It is also possible in some cases to make contact with someone through a mutual acquaintance - another professor in this case. Perhaps you can arrange an introduction in this way.
I don't think there is any methodical way to get someone's attention if they are busy - as most people are.
A circle of professional correspondents and, especially, collaborators is extremely valuable for any researcher. Sometimes it is possible to join the circle of one's advisor and expand from there.

I would not send reminder emails to remind someone to do a thing they have not committed to do; this seems quite rude to me. I'm most familiar with this behavior from spam marketing, predatory journals, and university administrators.
Some tips on sending emails:
How should I phrase an important question that I need to ask a professor?

Add to your email subject a term like "URGENT FOR STUDENT" or "YOU RECEIVE AN IMPORTANT MAIL". Next you can manage how to meet the teacher in person, or link with a person who know him, or have once received an answer from a teacher.
Have a good emailing.

I think the first sentence is resulting in the downvotes. I agree with the next.

When I receive messages with such headers, I delete these without reading.

I thought that the only people who used these sorts of subjects were spam/phishing...


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Points/Commas in mathematical formulas
the following question. I have a lot of mathematical formulas and equations in my thesis and now I was wondering if equations are separated with dots or commas. Would option 1 or 2 be correct now?
See here:
Pic

Depends on where in a sentence an equation is.

There are already many other questions on this forum that address this (which should also be easy to find), but the short answer is that formulas are treated as a regular part of the text, whether or not you offset them into their own lines. So, if you would add a comma or period if the formula was printed as part of the regular flow of text, you should add a comma or period in the same way if you offset the formula onto its own line.


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Pure Math in Industry
I have a bachelor's degree in math (from Ghana) and for my final year project, I did something in applied topology; I explored the application of persistent homology to topological data analysis. I am considering applying for a master's degree and would like to do something that directly relates to my undergraduate project. From my research, any master's program that offers algebraic topology will best suit my preference. However, I have no intention of staying in academia. While I enjoy research, I would rather work in the industry, full-time, and then do mathematical research as a sort of pastime.
I am currently learning to be a professional data analyst. I am worried that with if I do my master's in a pure math field I will struggle to transition to industry. Any advice for me will be most appreciated.

This is for a PhD: https://academia.stackexchange.com/questions/192634/what-non-academic-job-options-are-there-for-someone-with-a-phd-in-algebraic-topo but some parts of some answers still apply to your case.

This question is seeking an opinion-dependent career advice and is simply blatantly offtopic on Academia StackExchange. There is no needed to reward it with answers since such questions are popping up pretty often.

@Hasek If you feel a question is off-topic, you should vote to Close it. If you don't have enough rep to vote to close, you can still flag it with the same Close reason and it will be put in a queue for others to review. If you're noticing a pattern of a class of questions that should be off-topic but are not being closed as such, you might post on Meta to seek input/attract community attention to the issue. Please don't use other flags, like spam or rude and abusive flags, on content that is not spam or rude and abusive.

The other answers provide decent general advice. But I fear that they lack the urgency that comes from having actually done this recently. For some background, I was just hired in the past 8 months as an assistant professor at a research university for a data science position, was also preparing applications for industry data science positions, and specialize in topological data analysis (TDA).
Say you want to be employable for data science positions (careful: "data analyst" tends to refer a different job) and get an industry job in a reasonable time frame at the end of a masters. You can do this in a theory-heavy course. If you are unfocused, it will cost you time (and salary) brushing up on skills during a job search after you graduate.
What masters courses should you look for?
It is absolutely essential that you find a course that helps you further develop a set of computer skills in addition to theoretical ones that will be visible to potential employers. An anecdote: I took an MSc like this, the courses included a full proof-based run through of Hatcher's algebraic topology. But also multiple courses on modern machine learning with significant coding components.
What won't help you get a job that quickly: Taking a course with no computational lectures, writing a very theoretical thesis without further developing your computational skills, and then throwing in your industry applications against computer science majors who won't cost an employer 6 months of time just to become productive. This isn't hypothetical: I've had multiple friends get done with very theoretical math PhD's, including specializing in algebraic topology, then take 6mo - 1 year of re-skilling and searching to get an industry job.
What skills are your target employers looking for?
I'd encourage you: Go look up job postings for data science positions you think you could be interested in, and look at the list of skills they'd like.  They're going to include Python/R, SQL, probably familiarity with neural networks or CNN's, regrettably an increasing interest in Large Language Models, basic statistics, data cleaning, data visualization, etc. Some type of more serious memory management language like C++ couldn't hurt. Data science positions are also often interested in whether you have skills working with different types of data: Images, text/web scraping, "unstructured", geospatial.
You don't need to be an expert in all of them. Also, some demand for certain skills will change over time. You can't just blow building these up because they're all "easy", however.
What about more TDA?
If you take a Masters, TDA projects can be decent vehicles for a thesis that has some serious math while also benefiting from computer skills you want to build up to be competitive for industry position.
Computational topology and computational geometry for data analysis are not, however, particularly in-demand skills outside of academia. For example, persistent homology remains too expensive right now compared to much faster methods that are "good enough" for most use cases businesses currently care about. Obviously there's continual research work going into improving that situation, as the field is relatively young.
Which is to say, if you want to do more TDA/PH related work in a masters, but are angling for industry positions: It's something you can do, but you need to combine it with bulking up other skills.

In pure math, I'd guess that the job opportunities exist, but are very rare. Only a few companies can afford (in their thinking) to support pure research as it has no guaranteed payoff. Research in industry is mostly product focused, even at places like IBM, which does, by the way employ a number of "pure" researchers. But if it is hard to justify how you contribute to the "bottom line" it is hard to find a place.
Google, Oracle, IBM, and some others have a small (by the standards of the company) pure research staff, but most other companies don't, and rely on universities and government to make the fundamental breakthroughs that they ultimately depend upon. One major difficulty with pure research is that it is difficult to set time constraints. Insight and problem solution in math (and some other fields) doesn't and can't happen on a schedule. It happens when it happens if it happens at all. It is a risk, then, that a few companies are willing to take, but only in a limited way.
But if you want to do pure math as a hobby, sure, you can do that provided that financing your lifestyle doesn't take so much out of you that you have nothing left in you at the end of a long day.
If you want to do pure research and get paid for it, then I suggest that you keep open the possibility that it might be in academia. Even in a liberal arts (teaching) college you have more opportunity for pure math research than you would spending your days in a cubicle at a company in, for example, the auto industry. Just be realistic about what industry really cares about.
I think this is especially true for math and probably physics. In chemistry or biology it is probably a bit different, since, for example, the pharmaceutical industry "product" research can be pretty similar to pure research in those fields. Math, not so much.

Applied math, on the other hand, has many more opportunities, including in finance, insurance, and others. But, applied math is very different from pure math, with different techniques and goals. The same would be true for other applied fields - applied physics, for example.

What about cryptography? I consider abstract algebra to be pure and it’s certainly applicable, and I believe well beyond cryptography. I expect digital signal processing, logistics, and even sports are places where a pure mathematician has a decent chance of finding work - as long as they’re willing to dirty up their math with actual numbers. ;-)

A pure mathematics program will teach you two sets of skills, one of which will be highly applicable in any career you choose, the other of which may or may not be.

First and foremost, a pure mathematics program will teach you general cognitive skills with respect to rigorous thinking, problem solving, and how to deal with impossibly complex tasks by tackling tractable pieces.

Second, the specifics of the work that you engage in will teach you particular skills that may or may not be applicable anywhere else. For example:

Abstruse mathematical theorems and specific proof techniques will likely not be useful.
Learning how to search through research literature to find useful theorems and proof techniques is more likely be useful.
Even some pure mathematics may include computational exploration and data analysis, which is definitely useful.

Somebody hiring in industry will likely be interested in both what you can do right now and what your potential for growth looks like.

If you are learning and producing visible evidence of competence with data analysis somewhere (whether in your program or outside of it), that will give you skills for "right now".
The cognitive skills from your research program will more address potential for growth, if you can demonstrate flexibility of mind in how you apply them outside of the narrow context in which you've learned them.

Bottom line: there are valuable skills for industry available in a pure mathematics program, if you are flexible enough to understand how to apply them across disciplines.

A math degree is the last reason why anyone would struggle in the industry. While it may be country dependent, fields like data science/insurance/logistics etc. are full of people with math degrees and some positions even require one. I would argue that math is the best degree to have for those jobs, since understanding and using the correct math is the difficult part of the job. Using the IT tools (R, python, Excel, sql etc.) is the much much easier part.

More applied math than pure, though.

That is a fair point. I still don't think OP should be worried.

@Buffy They may not find a pure maths job, but a maths background will still be seen very favourably in many industries.

I second everything Buffy said, but my undergrad was in pure math and I started on a masters program in pure math before ultimately transferring to law school so I may be able to offer some relevant experience.
As Buffy said, finding industry jobs that are truly in pure math is very difficult. I never succeeded. But finding industry jobs that appreciate someone with a background in mathematics, even if it is pure mathematics, is quite easy. I held jobs in programming, database administration, and data analysis in more than one separate industry.
Also while my current firm certainly cares more about my J.D. than my work in mathematics, the skill of thinking rigorously is always useful and I even make use of basic mathematical concepts surprisingly often in my work as a lawyer.
To somewhat add to what Buffy said, I can say that trying to do mathematical research as a hobby while having a full time job is challenging to put it mildly. While I still enjoy mathematics, the closest thing I have managed to do to research since I transferred out of the master's program is that I can still help my son with his math homework even though he's in college to be an electrical engineer. (I did manage to publish a couple of articles about relational algebra, but those were in trade magazines for database administrators about applying relational algebra to practical database administration and did not really develop a new idea so much as summarize old ones with practical examples). Perhaps you are more skilled or more disciplined than I am. Fermat quite famously did significant research as a hobby while being a judge. I have not managed to live up to Fermat's example.

Effectiveness in industry jobs requires some software engineering experience. Math majors without significant demonstrated software development under their belt require a lot of on-the-job training by a peer, and that's assuming that the position even has such a peer available. Employers will not consider such an applicant if someone comparable but with developer skills is available.
If you didn't have code in several small projects reviewed by a professional, you'll face many hardships IMHO. I have dealt with numerical code written by grad students in math fields - and one thing novices have a lot of trouble with is tendency to copy-paste and inability to factor out concepts at different levels of abstraction. They even have trouble just figuring out what those levels of abstraction might be to start with - a fact perhaps puzzling given their math background.
I've seen plenty of numerical code written for Ph.D. dissertations by people without guidance/review by a software engineer that was an impenetrable mess that could be shortened to 1/4 the length while making it self-documenting, easy to understand in terms of the application rather than implementation details, amenable to unit testing, and so on.
If you can contribute to open-source math libraries/projects that have code reviews in place for contributors - that might be a good way to gain experience. I have no idea how much of it does with topology, though. Open source data analysis is all the rage though so it shouldn't be hard to find something where you could gain experience.
It helps already if you can "code your way out of a box" for simple repetitive data wrangling tasks - say scripting with Python, numPy and other relevant libraries. Even that is a leg up over some applicants. The ability to write maintainable code takes a long time to develop, so you won't be an expert in a year or two anyway. But at least some familiarity with industrial software development processes - a familiarity that can be built contributing to open source - will go a long way towards making you a more desirable candidate.