Dropping a Course in my Field Before Applying to Graduate School I'm currently a Senior Undergraduate planning on applying to MS and PhD programs this Fall.

Between my current research job, TA position, and courses I believe I'm over committed time wise this semester (only 2 weeks in and I'm very stressed). I was looking to drop a course to free up my schedule and unfortunately the best candidate seems to be a course in my research area in which I'm applying to graduate schools.

The class takes place during night hours, it's poorly taught/structured, and I seem to know most of the material already. I have publications in the area and I feel as they should make up for not taking the course, but currently dropping the course would show up on my transcript (as a withdrawal).

Would a withdrawal from a course in my related research area negatively affect my graduate application? If so, severely?

Currently I am having a discussion with an advisor, but I'd also like thoughts from others who are familiar with graduate admissions processes. There are several other related questions on this site, but all seem to address a large number of W's or W's in areas not related to their proposed research area.

I wouldn't worry about it. You have a perfectly valid reason for dropping. Your advisor can give the best advice, of course, knowing you better than we do, and also knowing other options you might have.

But a poorly taught course won't give you much of a push toward your goal in any case.

But if your advisor is "on board" with you dropping and supports you generally, then you should be fine for the more important task of getting good letters of recommendation, which will count much more heavily than a W on the transcript.


What exactly the reviewers mean by saying "the contribution/novelty is limited"? I sometimes receive a comment from 1-2 of my reviewers saying something similar to "the contributions and novelty of the work/method is limited".
Consider it in the context of CS where you develop methods and algorithms which increase the performance of the state-of-the-art in a specific problem/domain.

Of course in every paper, i state my contributions for the reader to understand, but when i receive a general comment like that i get puzzled. Especially when there is no mutual communication between the author and the reviewer to clarify the misunderstandings.

So, let's say, in what typical situation a reviewer might vote for a critism like that?

Possibly related xkcd. Please don’t take offence!

I’ve seen some papers in CS which validly improve upon the state-of-the-art on particular datasets. In fact, you might find these where there is an ongoing annual challenge, or a specific dataset that is used. The numbers are good but they essentially glue two existing methods together. Sometimes there are perfectly novel contributions in the "glue": sticking these two methods together only works if you do it in a particular way, but "glue" contributions are smaller than completely novel "method" contributions and this might elicit such a response from a reviewer. Even if gluing these two methods together is novel.

Usually, in these papers, the results are great (they win the challenge/dataset) and I always wonder why the paper hasn’t been cited more. I think the answer to that is that the citations for the "method" contributions will go to the original papers and the "glue" contributions, if novel, might not transfer to other techniques or fields.


How to submit a talk to a conference – Starting with replying to a Call for Presentations I have received a Call for Presentations for a conference and I have a topic that could be interesting to present.

The Call for Presentations describe the various forms of presentations that one can submit, and then links to a form asking for a title, presenter name(s) and an abstract. This has a deadline around two months before the conference.

Now, to my questions: As I mentioned, I have a topic in mind, but unless my presentation is accepted, I would prefer not spending a lot of time preparing a good presentation of this (technical) subject. Will the decision about whether to accept the presentation be done solely on the grounds of the submitted abstract or will they be in touch before accepting the presentation?

(This is a computer science conference, in case that's germane.)

It depends on how close you research topic/presentation is to the conference topic. I've never had an abstract turned down, but I present in sessions with topics relevant to my research. Most conferences wan't people to come and present, in general. Worst case scenario, it get's turned down. You will have a head start on the next abstract submission.


How to turn down an offer if you aren't sure where you're going yet? I'm a PhD applicant. I recently got an offer from a school I will not be attending.

The Professor that I spoke to politely asked that, if possible, I give an answer as soon I know if I'm attending the university.

I've already determined that I won't be attending, as I'm deciding between a couple other offers that I like better.  What's the best way to turn down the offer without explicitly mentioning where I'm going instead (since I don't know yet).

Regretfully, I am declining your offer. Thank you very much for considering me for your program.

  Best regards,

  ThatGuy

You don't have to say where you're going instead.


As a non-academic, how do I find papers that reference a certain article? I have recently read an interesting article on number theory, published in a well-known mathematics journal. As a keen amateur mathematician, I have tried to develop some of the ideas presented in the paper. Surprisingly, I have come to a remarkable conclusion that might be worth publishing. Naturally, I first wanted to make sure that this has not been published before, so I tried to find papers that referenced the original article.

The issue is that websites that index or catalog scholarly material are off-limits to non-academics. In particular, in order to find references to certain papers or authors, one must first log in with an institution's credentials, which obviously I do not possess. What are my options? Do I:

write up my research anyway and try to publish it, with the risk of wasting everybody's time if a similar publication has already been made,
waste the original author's time by asking him to send me a list of references to his article (with a high probability of having my request immediately discarded), or
pay a high subscription fee to these cataloging websites in order to find what I'm looking for?

Is there another way for me to go about this?

Google Scholar can get you quite far.

Go to scholar.google.com and paste in the full title of the paper you're looking for.  Here's one of mine:

Click the "cited by" link (highlighted) and you'll get a list of works citing the paper you're interested in.  Some of these will be papers, some won't, and Google's indexing isn't quite the same as some of the other sources.  But it's close.  For each of the citing papers, there's an "All n versions" link.  Browsing those versions will often get you to a legitimate copy from the author's institutional repository (see the right-hand column), ArXiv, or various other sources; the Unpaywall browser extension can also help track these down.


Transitioning from pure mathematics to artificial intelligence I am studying for a Master's degree in pure mathematics, but still have one year to go with my studies. However, I'm interested in specialising in artificial intelligence and pursuing a PhD. I would also like to work for a start-up that deals with education or robotics. How can I gain the necessary skills to succeed in transitioning from mathematics to AI?

Congratulations on knowing what you want to do! This already puts you a step above many people who are still unsure.
There is good news and bad news for you, at least based on what you've written.

The good news is that familiarity with pure mathematics can be your biggest strength: formal concepts in AI will come easy to you, even when they may be difficult for others. For example, you likely already have the skills necessary to understand matrix algebra, derivatives of multivariable functions, probability and basic statistics, etc.

The bad news is that there are a lot of others like you -- AI is a very hot field, which draws a lot of interest from undergraduate and master's students in general. This makes getting into good programs more competitive.

Taking coursework, including online if you can't take relevant classes as part of your master's program, should put you in a position where you can get into master's programs as well as possibly weaker PhD programs. While you are still studying math, take classes in probability and statistics. If you want to get into good PhD programs, you will need research experience, so the route to take is to first get a master's (for example) in AI, and try to get to know a professor during that time and do some research in a lab.
Whatever your route, having a good mentor to advise you for your personal situation is critical (I would almost say strictly necessary). So keep this in mind as you look at programs. A large program or online program is less likely to give you a good mentor. Even a mentor in a related area like pure math statistics would be much better than nothing.


Graduate courses or research experience for PhD admissions: Which is more important? I am currently at a crossroads, deciding between several MS program offers. I have the choice to do a research based masters, where I would take very few graduate courses but focus almost solely on research, and a more course based masters where I would primarily be taking graduate courses with a small thesis component. My goal is to leverage my MS experience to apply to US PhD programs. My field is applied physics.

Given these choices and my end goal, who would be more attractive to US PhD programs? A candidate with a few graduate courses under their belt but with 1-2 years of research experience (with a paper or two) or a candidate with no papers but who aced many graduate level courses?

I'd say the 1-2 years of research experience is the better option and here is why:

You are applying for a PhD position. Having research experience shows that you are already familiar with the challenges and you are not fresh. i.e. the department/lab/sponsor/etc. know that the learning curve is less for you. You may even bring or continue a project that you've already started. So, you may not need to start looking for a research subject either.

On the other hand, with all the MOOC, you can more or less learn or at least familiarize yourself with the some of the materials you need (I admit, this is not possible for every field or every course though).


Recommended depth of mathematics for PhD in machine learning I am planning to do my PhD in machine learning. From my personal experience, I found that mathematics is a primary way to make a good research, but I'm doubting about the depth of mathematics required. Most of the impressive inventions in most of the computer science domains comes from mathematics only. But if I spend most of my time in learning mathematics, then I may end up with wasting my time. So, how much depth of mathematics is recommended to carry my PhD? Shall I study only books on machine learning or core mathematics also?

Here is a somewhat different bit of advice. If you are about to apply, and your background is strong enough to get accepted and find a suitable advisor, then just do that now. Let the rest follow.

There is a lot of mathematics that can be applied to machine learning but until you have a specific problem it is hard to say which is best.

But, mathematics is a way of thinking as much as, and perhaps more than, any given topic. So any mathematics that you have time to study now, and for which you have any lack, or any hunger, will probably aid you in that way of thinking.

There are a lot of ways to think, math is just one. For someone in machine learning or any aspect of AI, having lots of ways to approach a problem is a big win.

But get yourself connected first and then look at the specifics of what you need to do.


Submission paper to a journal with no reply I submitted a paper ten day ago, respecting the dead line. Precisely, I submitted the paper two days before the dead line indicated by the Journal on its web site. The Journal had different editions during the years, such as the spring, summer and winter one. The deadline is important only to see your article published on the next edition of the journal. However, from the day I submitted the paper, I still haven't received any answer from the editors. I submitted my paper, as required by e/mail. I didn't get even a confirmitation that the paper was received. Is this normal?

Should I ask about the current status of the paper?

From what I understand, the journal has a schedule for considering articles to be published in the next issues. This means that if you miss a (not the) deadline, your article will be considered for inclusion in a subsequent issue. No problem there.

You not having heard from them in 10 days does not mean your paper was not received. I assume they received the paper already, but they 're too busy/unprofessional not to send back a confirmation or a thank you email.

Yes, of course you can ask about the status; nothing wrong can come out of that. I would write an email of the form:

Dear Editor,
I am writing to inquire about the status of the paper, entitled:
[title]
that I submitted to the [Journal name], on [Date].
I would appreciate if you could confirm that you have received the paper.
Best regards,
[Name]
[Affiliation]

Assuming of course that you have checked your spam folder.


Funding as a newly graduated PhD So at last I have graduated. I experienced my ex advisor 'dropping me' more or less, and focusing on his own tasks. He no longer communicates with me about anything relevant, and it seems my employment with the university as a research position will come to an end unless I seek funding alone.

Applying for industrial postdocs to get funding this way seems a little counter-intuitive considering I could spend all that time looking for an industry job. Alternatively, I've looked at grants but most things seem to be PhD age 2+. I spoke with a career counselor who confirmed this.

How does one continue working in the same environment if there's no support from their side regarding funding and I'm meant to find it alone? Is this a clear sign I should move on?

Firstly, congratulations on finishing! It's a big achievement.

If I were you I would be applying for post-doc positions and/or industry positions ASAP. It depends on whether you want to stay in academia or work in industry.

Think about what you want to do and don't get misled by your ex-advisors mixed messages - if he really wanted to continue working with you, he would help you find funding instead of leaving it all up to you. If you really are interested in staying with him then ask him if he could give you advice on what funding you can apply for as newly graduated PhD.


Advisor moving to a different school, should I move with him? I am currently a PhD student studying robotics at university A in my third year, and I've recently finished my qualifying exams and advanced to candidacy. My professor has been in contact with another university B, and they want to offer him a position there, so now I have two choices: stay at A, and finish up my PhD; or move with him to B, help set up the new lab, and finish my PhD there. BY the time I take this decision, I'd have finished three years into my PhD. The pros and cons are somewhat like this.

University A's PhD program is more research focused with minimal coursework. If I move to B, I am looking at an extra year/more of coursework, that probably will not be relevant to my research at all. Few schools seem to offer coursework that's directly in line with my research.
University B is better ranked; and if I move there, I will be part of a new research group that's just being set up. That could be a better platform, and I'd gain a lot of valuable experience being involved in a lot more projects.
Obviously, university B is a completely new place, new people etc. I've already spent about 5 years (master's+PhD) at A.
Based on what the university website says, there don't seem to be a lot of course equivalents of what I'd already taken in my master's/PhD so far in the new university.
Personally, given the amount of time I'd spent in this city, and other such factors, I would prefer to stay at A.

Given these parameters, would it be worth moving to B, how it would set me back timeline wise and the possible advantages from the move?

There's no simple answer here. Contrary to "The Fire Guy," when I moved, all of my students stayed at their old institution—primarily for financial and personal reasons. However, I received approval from the department at my old university to continue advising the students who remained.

The big question that you haven't addressed is what you'd have left to do to finish at school A. Are you one or two years out from graduation, or more? And how much extra time would you need to spend at school B completing requirements? And are there other issues, such as teaching load and organizational duties, that may extend your time beyond what you'd have at school A?

As far as being involved in a lot of projects, that's a double-edged sword: you end up with an experience profile that's a mile wide, but not very deep. It's not something you tend to be able to convert into a PhD thesis relatively easily.


Writing a paper from Master thesis, but not sure how to organize the paper or what to include in it I finished my master thesis and my advisor asked me if I would like to publish a paper on it. Being my first paper, I was very excited when he first told me, so I immediately started reading the journal rules about how to write it properly.

Now I am at the point in which I should start writing, but I actually do not know how to organize things. I asked my advisor and he said it should be a sort of summarisation of the thesis, but I fear some contents of the thesis, that were new to me at the beginning, might be taken for granted from research people and so I will actually write obvious things.

Also, I have some contents which, I think, are quite interesting in the sense that I saw only one other paper using some of these concept, but without providing any information about the set-up of the code, which is something I would like to add since I feel it might be useful to have an example on how to do it, especially if you are at the beginning as I was when I started the thesis. Still, the idea is not new. So, how to deal with this? My feeling at the moment is that the paper does not worth the effort.

You could structure your material as follows. To my perception (at least in computer science) this is more or less standard structure.

Introduction: The bigger picture in which your approach fits in: the problem you are trying to solve. A short description that guides the reader from the more general to the more specific issue.
You could have an extra section regarding some preliminary concepts that are crucial to understanding your work. The nice thing with journals is that you do not have the space restrictions that you have in conferences, and thus you can expand a little.
Related work: Attempts that already exist in the literature, and how you compare with them. Why your solution would be an advance in the state-of-the-art, and under which conditions.
Description of your work: as detailed and as formal you can be regarding e.g. a system architecture, an algorithm, the core idea of the paper.
Performance measurements. A comparison to "competing" approaches in terms of performance.
Optionally, a discussion section, where you discuss about your experiments. This could be merged in the previous section.
Conclusions and future work. A section where you provide your most important findings and give directions towards which your work can be expanded.

Of course, the above are merely indicative and do not apply to all cases. Personally, this is how would start drafting a new journal publication.

  My feeling at the moment is that the paper does not worth the effort.

It is certainly worth the effort. Why not? Even if the idea is not new, your paper would be a study on how your approach performs in certain conditions.

Funny as it may be, this presentation gives some quality advice: http://research.microsoft.com/en-us/um/people/simonpj/papers/giving-a-talk/writing-a-paper-slides.pdf


Rankings for university departments The Internet is littered with information about university rankings and comparisons that it is very easy for an applicant to lose track of what exactly he wants to know. Apparently rankings which compare all departments of a university and have a single rank are not very useful from an applicant's perspective. Yet these are the most common rankings one can find. I know of the FT rankings which order MBA schools. It does rank B-schools in terms of research too, but clumps different branches (Accounting, Finance, Operations, etc) into one.

For other departments are there reputable and reliable portals where one can get to know rankings based on various criteria like published research, number of graduates, time taken for graduation, etc?
Are there no well-established ranking systems for academic departments? For example, these ranking systems could be similar to those in cricket or football. Such as periodic updates are made with every publication and every citation, and possibly journal reputation could also be brought into picture. In a way, would that not enhance the competitiveness in academia among similar departments?

The big difficulty with devising formal ranking systems based on numerical measures is that, outside of a handful of areas like sports, anything we can measure is at best a proxy for what we really care about.  It may start off as a pretty accurate reflection, but anyone judged on this basis will quickly discover how to manipulate it.

For example, universities in the US are often judged partially by the fraction of students they accept.  Of course, ambitious universities have adapted by advertising to encourage more applications, with no intention of accepting these applicants, but just to lower the acceptance rate by increasing the denominator.

Similarly, universities are also judged based on "yield," the fraction of admitted students who attend.  That sounds at first like a pretty good measure of popularity, but it creates an incentive to game the system by rejecting students you think are likely to choose another university in the end, and universities do just that.

Time taken to graduation can be gamed by kicking out students who are taking too long.  Employment rates can be gamed by offering ten-week temporary jobs to unemployed students, timed to coincide with the measurement of employment rate (really!).  All sorts of things can be gamed.

And this is not just a theoretical problem.  It occurs all the time in practice (if you are in the US, then your school is very likely doing some of these things), and some people are extremely upset about it.

You might think scholarly measures based on citation counts would be less subject to this, but they are not.  There are plenty of corrupt journals where editors put pressure on people to cite papers, or even use totally fraudulent methods like publishing review articles that carefully cite every paper they have recently published.  People have been caught seriously distorting their journals' impact factors by doing this, and I'm sure there are other, more clever editors who are getting away with it.  If professional success depends on influencing a number, then people will discover ways to influence it.

In summary, there's an awful tension between transparency and resistance to fraud: if you explain how your ratings work, then people will manipulate them.  Nobody has any idea how to avoid this, and the net effect is that serious scholars do not waste time trying to compile numerical rankings.  For the most part, the only people who do are those who are naive, trying to make money in unscrupulous ways, or trying to promote a cause through carefully chosen rating methods.  The rankings they produce are not worth paying attention to.

P.S.  Polls of expert opinion are generally much better than rankings based on numerical measures, but even they have their problems.  For example, the U.S. News college rankings are based partly on asking college presidents to rank other schools.  Clemson University manipulated the rankings by rating all competing schools as below average, no matter how good they were.  I suspect they weren't the only ones to do this - the amazing thing isn't that it happened, but rather that we ever found out.


Paper accepted three hours after submission I have submitted a paper to a conference sponsored by IEEE. After 3 hours, i received an email that said

  your paper has been accepted and recommend to present paper. Your paper will be recommended for publication in IEEE Xplore�. The conference date is after 23 days.

Note: I didn’t receive any reviewer comments on my paper. Only 6 points are written which seems to be general for every paper.

Is it really possible that they have reviewed the paper or did they just accept the paper without review?

It's impossible that the paper was reviewed (at least by a human). 3 hours is way too short. Weeks is a much better order of magnitude estimate for the time required to review papers.

It sounds like you're dealing with a predatory conference. I would approach with care. Check if the conference's scientific committee are actually established researchers in the field, check the keynote speakers to see if other established researchers are intending to attend, check if the conference has been held before and if so, how those conferences went.


Plagiarism-checker to prevent self-plagiarism I am working on my PhD thesis and have to write a monograph based on my publications. To prevent that I plagiarize myself, I came up with the idea to automatically check my thesis against my publications.

Is there already a tool for this work-flow?

  Input: PDf of thesis and a pile of PDFs;
  Output: annotated PDF of thesis.

Summary:

Make sure you understand the standards of self-plagiarism relevant to your project.
Your institution's LMS likely provides access to an automated plagiarism checker you could use.
Automated plagiarism checkers are imperfect.
Some people think that commercial plagiarism checkers are unethical.

If you haven't already done so, talk to your dissertation committee about their understanding of self-plagiarism. What exactly are they concerned you might do? What are their standards, and are these standards reflective of common standards in your discipline?

Once you know how your committee understands self-plagiarism, talk to the people who administer the learning management system (LMS) at your campus. Many LMSs include access to commercial plagiarism checkers, and many of these services allow teachers or students to upload selected papers to the plagiarism checker's database. (The idea is that the plagiarism checkers allow teachers to check student work against unpublished papers that other students--like roommates or sorority sisters--have submitted in response to the same assignment in a different section of the course). If you put your publications into the plagiarism checker's database, you should be able to use the plagiarism checker to identify potential self-plagiarism.

HOWEVER, automated plagiarism checkers are imperfect. Whichever one you use, it will probably flag some stuff that isn't really plagiarism, and it might miss some stuff that is. The automated checker should supplement your own judgment, not replace it.

Finally, some people think that commercial plagiarism checkers are unethical because they collect papers and make money off of them without paying the authors. The courts have ruled that it's fair use, but you should at least know that the issue exists.


Are academic indicators (h-index, impact factor, etc) really adopted by institutions? Currently used? And how? Academic indicators (h-index, impact factor, modified h-indexes, etc) have a long string of criticism by academics and not (see here, here or here).

Instead of debating (endlessly) on how "not representative" and flawed these numbers are (or howling generic rants...), I would be interested to know if Academia.SE community members have objective facts and reports on how academic workplaces are currently using these indicators. For instance in the upcoming REF2014 (UK), academics in my institution are urged to use, as their contributions, the papers accepted in highest impact factors journals.

In summary, can you trade your h-index for a better paying job?

On the European research market, various opinions have been offered on the use of h-index by ERC grant evaluation committees, and the broader relationship between h-index and ERC funding success. People who provide such opinion can be classified in three categories:

affiliated with the ERC
consultants whose business is to offer advice to (potential) candidates
individuals involved in another way, whose advice is most probably only anecdotal

So, what do the first two categories have to say? The official word from the guidelines is, well… absent. But it is politically correct to assert that h-index is not a good indicator of scientific quality and, as such, not used. This gives quotes like:

  Quality in science is not proved by accumulating quantitative points. The role of commercial impact factor and h-index is limited. Overemphasizing of publish or perish policy leads to a gradual perishing of all.

On the other hand, practical advice might be a bit more nuanced:

  There will also be a new h-index study the successful 2011 awardees in the PE and LS domains. The h-index is regarded as a background indicator rather than a determining factor and the study on the 2010 Advanced Grant awardees showed that each panel made awards across a considerable range and that there were significant differences across the different ERC panels. There was a big variation between different disciplines within the main domains.

and this:

  Every applicant had to choose his 10 best publications published in the recent decade and add how many times each of these papers were cited in the literature. The total number L of these citations describes well how the scientific community perceives their recent achievements. These numbers were provided by the ERC in the dossier of every applicant. However, during the evaluation process the panel did not put much emphasis on any bibliometric data. It was the opinions of the experts which did matter, not the bare numbers. Only after completing the evaluation process, I realised a correlation between these data and the final outcome.

To give another perspective: in France, a new evaluation system for higher education and research was put in place 5 years ago (the newly created agency performing the evaluation is called AERES). AERES evaluates each research group every 4 to 5 years, in order to give it an overall rating, which could be A+, A, B or C. This had at least two very practical consequences that I know of:

For yearly financial negotiations between each university and the Ministry for research, the ministry started to require a spreadsheet with the number of university teams rated A+, and the number of A team (B and C didn't seem to count). Financial support was then dependent on that number, at least as a starting point for the negotiations.
It became customary to include this grade in your French grant applications, because a A+ rating was considered a serious advantage. This was written in the “rules”, however…

So, all in all, are bibliometric and, speaking more broadly, academic indicators really adopted by institutions? Hell yeah! They make deciders’ job easier: quantification of research quality makes it easier to make decisions. Smart decision makers do realize, however, that a single indicator does not make for good decisions.


I am preparing for my PhD applications, and my referee passed away unexpectedly. What should I do? I am now preparing for my PhD applications to be submitted soon later this year. I recently was informed that one of my academic referees passed away quite unexpectedly.

The late professor was a great teacher and a wonderful person all round, in addition to being very well respected in my field. After recovering from my initial response to the news, I now have to decide who could write for me instead. I consider myself having done quite well and participated actively in the professor's class, hence the letter would have been quite positive.

I have considered asking my other tutors who have taught me in university, but I had not done as well and we were not as close, so I don't expect there would be credible or indeed useful details to be included in their recommendations. I have also considered asking my current supervisor (at another university) to write for me, though I have only been working for them since this summer.

Therefore my questions are:

What should I do and expect now?
How should I bring this up in my applications? Or should I? I certainly would consider it crass to include him anywhere on my CV or personal statement.

I have read discussions on situations where advisors passed away (e.g.1, e.g.2), but think my situation here is a little different, since I am still in the process of preparing for my applications and this was the first time he would write recommendation letters for me (he did not leave a letter, obviously). Hope this is ok.

I hope this question does not come off as insensitive, as I am at a loss thinking what to do next.

I had faced with a similar situation, some time ago:

As an applicant, you should have a list, including people who are
    qualified in your view to present LoR. This list should be sorted
    based on the priority, reasonably. If one assumes the late professor
    as the first person in the list, your exclusive option is just
    choosing the next one. I just contacted the other professor, who was aware of my collaboration with the late professor and knew
him
    for a couple of years. So, he, not only, did his best to show off my
    characteristics, but did try to depict some points about that famous
    professor and justified his tendency to work with me, as a bonus for
    my personal and technical features. In other words, all he did was
    presenting me as a right person to admission committee, in both his
    own view and the late professor's view (virtually!).

Therefore, in my estimation, if you are eager to be known as a person with a sort of related connections to that professor, just
try to be recommended by one, who knows you both and is aware of
your collaborations (at least in some aspects)
Typically, applicants mention the name of their supervisors within
the CV, in addition to their references. If the late professor was
not your supervisor, one might not consider citing his name in CV,
as a reasonable stuff. However, assume that you have passed a course
with him and had presented a seminar-based project with a report as
its outcome. You can mention that report within the scientific
accomplishment section in your CV and add a (Supervised by: Prof.
XXX), in front of it. It's all to show that you have the
experience of collaborating with that man, at least within a
short-time research project.

Furthermore, you can elaborate on his effects on your research
methodologies and personalitywithin your SoP, undoubtedly.

Good luck with your application!


Test answer rejected for saying more than asked for this is about a phenomenon often seen, but I came across a probably more prominent example recently, so I thought I'd ask for the community's opinion.

Here's the situation:
In the past semester's exams there was a question in computer architecture asking for X, and I, and many other students, answered Y, which contains X and is mostly revolving around X, thus successfully answering the question (in our opinion at least). The professor rejected the entire question (20% of the grade) and at any attempt to talk to him he directed us to the sample answer, stating that we "are wrong", we "answered another question, not the one asked", and so on. The question wasn't even given partial credit, and that happens systematically in his tests.

Another similar issue on the same test, was when a very few of us optimized a small bit of code in a way that would, industrially, be significant, and that part was rejected similarly, without any explanations, as "being entirely different". Explanations were also written on the test, so he cannot claim we did it without explaining.

This seems to be a general policy of the professor, as numerical errors are similarly crippling to grades, and in the sequel lesson, of an amphitheater of about or more than 80 people (and of course a lot more don't even attend classes as they are not obligatory, but I don't know if they're better or worse than those who attend), only 11 had succeeded the exam.

I would finally like to note that some of the stuff he rejects, he himself does in class, the most obvious being a computational error, and frustratingly he also did the optimization he rejected in the test.

So finally, the question:

I understand that professors have the right to grade based on their own criteria, but

Is it justifiable to reject an entire answer because more was said, without even partial credit?

Is it justifiable to reject an answer that gives something better than what is required, perfectly equivalent, even if relying on the student's intervention? (in this case, it would be a compiler optimization, thus not an entirely direct interpretation, but an elegant and industrially valuable workaround)

Is it acceptable to direct students to example answers and not allow them to explain why they think their answer is correct?

I'm also itching to see how we could deal with this, but that's not the main question so I leave that to your own discretion and benevolence to answer.

I know from personal experience that it's very frustrating not to get proper credit for knowledge that you think you demonstrated correctly, so let me start by saying I sympathize with your situation. Now let me address your questions:

  Is it justifiable to reject an entire answer because more was said,
  without even partial credit?

To give a literal, completely general answer: obviously yes. If the correct answer is, say, one sentence long, and your answer quotes the complete works of Shakespeare that happen to contain that sentence, I personally would not give you any points (but I would still be very impressed :-)). Of course, that is an absurd example, but it illustrates the point that what likely matters to your professor (or if not, certainly what should matter to a reasonable professor) is that your answer demonstrates an understanding of the topic the question asks about. It is certainly possible, and happens pretty frequently, that an answer Y contains the correct answer X but by adding more irrelevant things demonstrates a less good level of understanding than just the shorter answer X. The way this typically happens is that a student doesn't understand the material well and upon being asked about a certain topic, regurgitates everything they memorized about the topic, not knowing which part is relevant to the question. Clearly that does not leave a very good impression.

Now of course, the above does not address what happened in your specific situation. It is completely possible that your longer answer Y still demonstrated as good of an understanding of the material as the official answer X, and deserves to get partial or full credit; or not - you haven't given us enough information to say.

  Is it justifiable to reject an answer that gives something better than
  what is required, perfectly equivalent, even if relying on the
  student's intervention? (in this case, it would be a compiler
  optimization, thus not an entirely direct interpretation, but an
  elegant and industrially valuable workaround)

I think this question is too specific for anyone here to be able to answer without knowing more details. As I said above, the points you get for the answer should be correlated with the perceived mastery of the topic being asked about that your answer demonstrates. If including the optimization demonstrates that mastery, you should get the points. But I can imagine a situation where using the optimized method instead of the official method actually demonstrates less mastery, for example if it appears that you used the optimized method to avoid revealing that you don't know the simpler, less sophisticated method the question asked about. In that case, it may make sense for your answer to be rejected.

  Is it acceptable to direct students to example answers and not allow
  them to explain why they think their answer is correct?

I think it may be acceptable in certain cases. For example, if you have already asked many questions of the professor, argued with him in a way that he thought was unreasonable, overly argumentative, or delusional, and if the professor is very busy, for example is also teaching a class of 500 students in addition to the smaller class you're taking with him, at some point he may decide that spending more time discussing your exam with you can limit his ability to perform his other duties effectively, and be a disservice to the other students.

Summary. As you can see from my answers above, whether the professor's behavior is unreasonable depends on many details that you haven't provided. It's certainly possible that it is unreasonable, but it's also possible that it is less unreasonable than you may think. Statistically speaking, I've found that students often overestimate the extent to which they understand the material and the degree of correctness of their exam answers. That doesn't mean it's what happened in your case, but it's a possibility you should keep in mind as you consider your next steps. In any case, good luck! As for what steps you should take to address the situation, this answer is already quite long so I think it makes sense to save that for a follow-up question if you care to post one.


How to proceed when advisor asks you to resign? This happened with a friend of mine and I am asking this question on his behalf.

My friend was recruited for a sponsored research project in the university and offered a part-time PhD along with that. He is currently completing his coursework (2nd semester) which is pretty exhaustive. He has been working pretty rigorously and one of his poster presentation was accepted in a reputed conference within 6 months of work.

Since the coursework in this semester was tough, he wasn't able to give any tangible output in terms of publications or concrete results. Now, suddenly he has been asked by his advisor to resign. This has come to him as a surprise since he was expecting some scolding as his guide is known for being arrogant (and he is very reputed researcher in the field).

He is unsure whether to leave PhD or how to proceed and convince his advisor.

I believe he is pretty new (1 year of coursework) to the domain and since the project is pure research, he does deserve a chance. But I am unsure of how one should proceed in such cases.

EDIT (based on queries in comment)

Part Time PhD: He was recruited as an employee for the project by the university. And also offered a PhD. The stipend comes from the project cost(funding agency) and not from university funds (as is the case for direct PhD recruits in my university).

Advisor being Arrogant: He is very knowledgeable and reputed but known for scolding students frequently.(Some faculty members also agree to this including my own advisor)

This is a difficult situation. Regardless of the facts of the matter, there appears to be have been a breakdown of trust/communication: either the advisor has been sending signals for a while that the person didn't pick up on, or the advisor is indeed pulling this out of the blue, which also signifies a breakdown of communication.

Probably the best first thing to do is for the student to set up a meeting with the advisor to understand the advisor's reasons. It's possible that a face-to-face meeting might allow the advisor to express their underlying concerns in a more productive way that could lead to a concrete plan for activities.

If that fails, or the advisor refuses to meet, the next person to involve is usually the chair of graduate studies at the department (or the equivalent). There is usually someone whose job is to oversee the graduate program and manage conflicts like this. They might be able to mediate a rapproachment between the advisor and student (or even find another advisor for the student). I've had to do this in the past when I served in this capacity.

Bottom line: if the facts are as you outlined, then I think the student deserves a more reasonable explanation from the advisor as to why they're being terminated. Sadly though, in my experience  I've often found that students ignore signals being sent to them, and that advisors are unwilling/unable to give direct and clear feedback.


Pointing out language mistakes in a review In one of the reviews I received from a journal on a paper I sent there, there was a complaint that the paper contained a lot of language mistakes, so serious, that they change meaning of phrases. But no examples of such mistakes were provided.

I am not claiming that language in the paper was perfect, though the paper had been reviewed before submission by four different people (PhD's) with a good level of English and also by a native English speaker (not a scientist though).

I am confused about this quite strong statement about the paper's language despite the reviewer having perfectly understood almost all the ideas in the paper.

Just wondering: is it really typical for reviewers to point out language mistakes in reviews in this way?
a
(P.S. Just in case, I am very grateful to the reviewer for his or her very valuable suggestions on other formal mistakes and for giving me some new ideas).

If a reviewer has problems to follow the arguments and feels that this is due to the use of English, they usually include this in the review. Unfortunately, it is sometimes hard to pinpoint exactly what is unclear about the language, but a good reviewer should make some effort to help the authors. On the other hand, if the overall recommendation will be "reject" many reviewers do not invest the time to write down all unclear/wrong/misleading formulations. Note that a good reviewer may still follow your arguments although the language may be quite misleading (by just knowing what you are trying to show and correcting the wording while reading).

So you may ask for clarification (and I find this a reasonable request), but you should not expect to get an answer if the paper has been rejected anyway.


What to expect as a supervisor's first PhD student? Very much as the title states; what should I expect being my supervisor's first PhD student?

I understand that there are likely both pros and cons to this - however I'm looking to hear the experience of others, and how this compares to experience of those who have had a supervisor with previous students.

I was my supervisor's first Ph.D. student and I survived the experience...

Although your advisor is almost certainly trying to do a good job, the simple fact is that neither you nor your advisor has ever done this before so mistakes are likely.

My advice would be to make sure that your dissertation committee includes some more experienced faculty and get them involved in the process (e.g. by having meetings with your full committee, having the committee review dissertation proposal and attend presentations that you might give on the work) as early as possible.


Necessity of MoU for researcher exchange or collaboration In my research institute, there is a fair amount of discussion around wanting to collaborate or create exchanges with other labs, especially outside of the country (we are in Asia).  From myself and a few other researchers, we have connections to labs in the US from graduate school and previous research.

On my own initiative, I exchanged interns and research assistants between my lab and one in the states.  It was specific on the people we had, such that a student interning in my lab would benefit from going to the lab in the states and vice versa, as we worked on a common project.

The higher-ups want to make some "official" exchange, which may be some asian culture at play.  From this, the conversations are always around signing a memorandum of understanding (MoU).  For the exchange I worked on, we had specific people and specific goal, so a general Lab to Lab MoU did not seem as relevant as a mutual agreement of what the purpose of the exchange was.

My question is, how important or useful is an MoU, are there pros and cons to this?  I don't think it has any legal weight, so does anyone have experience with this type of collaboration and see why it is necessary in an overall scale?  Is signing an MoU the only way for an institute to call an exchange 'official'?

MOUs serve several purposes in my experience. First, they put down in writing what the two labs have agreed to do together. This formalizes the relationship a little and allows the participants to refer back to their commitments. Sometimes people get busy and forget to do some parts of the agreement. The MOU allows the other side to gently remind their partners that everyone committed to something and ask them when they will start on their part. People don't generally intend to shirk their responsibilities, but academics and researchers get busy, so informal and unpaid promises are first to slip. MOUs which formalize the relationship are often enough to make sure each side holds up its end.

Second, they allow others inside and outside the respective organizations to recognize that the relationship exists. Labs have directors, their directors have bosses (often department chairs, deans, or research vice presidents), and their bosses have bosses on up the chain. Having an MOU between labs establishes a formal relationship that can be recognized by these people. In fact, depending on the organization, the boss of the lab director may have to sign off on the MOU (not just the lab director themselves), which gives the agreement some level of officialness.

This official recognition generally doesn't get used for much more than promotion of the relationship between the labs. Though, it may also serve as the first step in a deepening relationship between larger parts of the overall organizations (departments, colleges/schools, and even whole universities or government entities). These formal relationships can also be helpful in pursuing government funding in some countries to help support the collaboration.

I've never used an MOU to formalize a simple summer student exchange. Those, in my experience, were always done with a verbal or email agreement. However, everything longer than a summer or more complicated than a student exchange that I've been involved in has had a written MOU. They lay out the expectations and give the signers cover with their higher-ups when the questions come about what two groups are doing together.

It's not generally seen as a contract, but does up the seriousness of the relationship to have an MOU.


How are mathematics PhDs from the USA viewed in mainland European universities? Do American-educated mathematics PhDs generally have an advantage over natively educated PhDs (in, say, France or Italy) in applying for research positions?

Do American-educated mathematics PhDs generally have an advantage over natively educated PhDs (in, say, France or Italy) in applying for research positions?

Generally speaking, no: why should they? And for what concerns specifically Italy, take into account that some things that can make an application strong(er) in the US (e.g. stellar recommendation letters or awards), might go totally ignored by a selection committee in Italy. This means that you should tailor your application to the country you're applying to. Let's see why.

A selection process in Italy, both for permanent and non-permanent positions, is usually composed of one to three steps, where each step counts for a fraction of the final mark (details vary across fields and position levels):

CV and publications (this step can count from roughly 30% to 100% of the final mark). The selection committee, before knowing the candidates, decides how to evaluate each section of the CV. If, e.g., the call doesn't explicitly mention recommendation letters, these are typically discarded; if the committee decides that awards count at most 2 points out of, say, 30, even if you have a Nobel prize, they (probably) won't add more than two points (should they do, in case of appeal from some of the candidates, the call can be invalidated).
One or two technical essays (optional; when present can count from roughly 30% to 50% of the final mark). Here, the applicants are typically required to write a technical essay on a specific topic in the field of the call. I don't know about mathematics, but in my field one might be required to describe how a certain instrument works (e.g. a spectrum analyzer) or how to perform a certain experiment (e.g. how to measure the fine structure constant). The difficulty of the question depends on the level of the position.
Interview (optional, depending on the position). Here the applicants are typically asked to discuss technical topics in their field or, for instance, some of their publications. For an associate- or full-professor position, this step can be substituted by a (public) lecture on a specific topic in front of the selection committee.


Is it appropriate to email a professor about something unrelated to class? I have a professor currently that I have previously had in a class on a different subject.  Would it be appropriate to email them with a link to an article I found that relates to the previous class' subject (but not to the current one)?  There are no longer office hours due to school moving online, but I'm unsure if an email would be considered a waste of time because it would essentially just be sharing an article that she may have already read.

I assume you mean something related to academics rather than a completely random meme/cat video.

If so: yes, it’s totally appropriate. Discussing cool research topics with students, especially when it’s free from the context of grading, is one of the most fun aspects of a professor’s work. I absolutely love it when students email me links like that.

And if the professor thinks it’s a waste of time, they’ll reply with a curt thanks and ignore the link, or simply ignore your email altogether.


Attend an international conference during PhD? How to know I'm ready (or not) to attend/answer a call for paper? I am currently a Ph.D. candidate (1st year). I am still reviewing literature and thinking about the design of my research to begin the empirical part within 4–6 months (social sciences, qualitative research).
There is an international conference organizing different seminars about themes I precisely deal with. Though I still did not start the empirical part, I would like to know if it is of any interest to answer a call for contribution to discuss the theoretical framework I contemplate to build upon?
My supervisor seems not knowing yet that this conference is organized (his research interests are quite far from that of the conference). I fear it might sounds being fiercely ambitious, though I consider this meeting as an opportunity to meet people sharing my research interests/challenging my first assumptions.

From my experience, it's much more useful and rewarding (even for people following your talk) to present a non-finished work than a completed one. As you said, people may come with an interesting opinion or fact you didn't think about and that may improve your study. Moreover, I found much easy and stimulating to talk with someone asking for suggestions rather than presenting a finished work. In this case, they're probably not interested in my opinion anyway.
I attended couple of big international conference and the talk I remember the most was from a guy who didn't even went to the field site to collect samples yet :)
That said, if you think some people may be interested in copying your work and publish it before you, that's a big no-no.


Should I accept the job offers before having the results for my grad schoold admissions? I currently have a bit of a conundrum - I would like to do a Masters right after undergrad and am currently applying to programs. However, I have several job offers. In case I do not get into the programs I want, should I accept the offer now? I'm not quite sure how to handle the situation.

Are the jobs and Master's programs in the same city? Many graduate programs are designed around the idea that many participants have 9-5 jobs. It may be possible to do work towards a Master's while employed. If you think that would be too difficult,  you might be able to ask your employer whether you can drop down to part-time status. Another option is to look into delaying the start of the Master's program for a semester or two, so you're not leaving the job right after starting.


How to self-advocate after rotation rejection? I am a first year PhD student. I just finished my first rotation lab and absolutely loved it. I met with my PI every week, and I never felt like she had a problem with me. Though I never had a frank discussion with her regarding the fact that I wanted to commit to her lab (big mistake, I know), I heard from other students who spoke with her that she wanted to make sure she had enough space in the lab for me and the other rotation students had we chosen to join. She also promised us that she had enough funding so we wouldn't have to compete with one another. I really thought I had found my lab home until....

I received an email from her yesterday telling me that she couldn't keep me. The reasons she gave me seemed flimsy (i.e. I wasn't enthusiastic enough....when literally anyone else in the group can vouch for my enthusiasm!) and contradicted what she told me at the start of the rotation (ie. she said she wished I had gotten further in the project, when she explicitly said multiple times that she didn't care how far we got through our rotation projects).

I hate confrontation, but I want to be a self-advocate. My biggest regret in undergrad was not self-advocating enough. I feel like I lost a lot of wonderful opportunities because of my fear to approach professors. But this time I want to stand up for myself, try to get a chance to defend myself and ask for legitimate reasons why she suddenly rejected me. And of course, try everything I can to try to change her mind. This is my dream lab, and I want to be brave enough to fight. I know I'm stepping on some annoying lab politics by doing this, but how should I go about addressing my concerns with my PI while not offending her?

If you are in good terms with your PI and you have weekly meetings with her, it is probably best if you bring up the topic in your next meeting. If "lab politics" are really involved (which need not be the case), it is unlikely that she will provide more information through email.

If she had told you that she would have funding and space for you all, it is perfectly reasonable for you to ask what changed, so she should not be offended by the question.

Regarding how to phrase the question, I would be moderately direct but, as most "conflict management" courses suggest, prefer the use of "I-messages" instead of "you-messages". Something along the lines of:

  I would have liked to be able to stay here, is it really not possible anymore? And if it is not, do you have any suggestions as to how I can do better elsewhere in the future?


What is the proper designation for someone who has not finished their master's degree? If a person has completed their course work but has not submitted their dissertation or capstone project. what can you call them if they are teaching a class or course at an academic institution?

In the US, a Master's Degree does not grant a title. So, their title would be the same as if they had completed their degree. This would probably be Ms. Smith or Mr. Smith. Some institutions might have other titles. For example, a military academy would use ranks (e.g., Captain or Major Smith).

Also, the title of the person would vary by institution. My guess is the most common title would be Instructor.

Based upon a comment by cag51, the title of Professor would also not be appropriate unless the instructor held a position with that title, which is unlikely.


Do I need to repeat the books quoted in footnotes in the bibliography? When writing an essay, if I have cited some paragraph from a book in the footnotes, do I need to include that book in the bibliography after the essay? Or I only need to include books that have not been cited in the bibliography?

If you are using OSCOLA style, then the footnotes will include all the necessary bibliographic information and a bibliography/works cited is not required.  See, for example, the Oxford University Undergraduate Law Journal, which follows OSCOLA style.

Generally speaking, most styles that require a Works Cited/Bibliography have "in-line" or "parenthetical" citations, rather than footnotes (e.g., APA style). The key thing to remember is that the purpose of citations is to allow your readers to consult your sources for themselves and, where you are citing secondary sources, to give credit to the authors for their contribution to the scholarly conversation.


How frequently should PhD students update their advisor? I'm a 1st year phd student in math. My advisor adopts a more ambiguous meeting policy that I approach him whenever I want to talk. So I was sending him long emails on a biweekly basis with updates of what I read and thought about in that duration, out of my own initiative. Of course, a lot of it is not substantial progress and probably doesn't even worth reporting. I do this because the actual meeting is sporadic and the frequency is definitely below once per several months. So I feel that I should update him on a regular basis. My advisor didn't say stop sending the updates but just stopped responding to them. I guess it is kind of annoying seeing a lot of stuff from your students without actual breakthrough. Before I ask him anything, I just wonder what would be the norm of phd student updating their advisors regarding frequency and whether they should update without any actual progress.

It depends on your situation. I meet with each of my students once a week via Zoom. When I was a student, I met with my advisor rarely. I only gave him finished results.


Citing a Datasheet or Technical document in IEEE style I am working on a thesis in the Engineering field and I came across the problem of citing datasheets, technical documents, test reports, application notes, reference designs... that the manufacturers of integrated circuits publish.

The problem to cite those documents in IEEE style are:

Usually there is no author, but a company (e.g. Texas Instruments)
These documents are not a book, article... with a clearly defined citation template
They are usually identified by a code (e.g. LM555) that corresponds to the integrated circuit part number. Finding the document on the internet is easier searching for the specific code (LM555) than for the title of the document or part (Highly Stable 555 Timer)

Therefore, I think that incluiding the part number or code is essential for providing a good reference, but I don't know how to insert it in the citation, plus, reference tracking software like Zotero or EndNote do not include any special field for storing this codes.

So far the resources I have found on the topic are:

Monash University datasheet citation gide in IEEE style
Tex StackExchange question on standards and datasheet citation with no author
Victoria University standards and patents citation guide in IEEE style

For further clarification, the types of documents I would like to cite are:

Datasheet LM555 http://www.ti.com/lit/ds/symlink/lm555.pdf
Test Report PMP21771 http://www.ti.com/lit/ug/tidt117/tidt117.pdf

Any suggestions on how to cite appropriately these documents or how to store them in a citation software will be very much appreciated. Please, share your views on the topic.

I cite datasheets as manuals. For example, I recently cited the LTC3600. My experience is with biblatex and JabRef. In JabRef I can choose the 'manual' type and can fill in the organisation, datasheet title, url, and use a (part) number. I believe that most citation managers have a field for 'number', you can use this for part codes.

The resultant biblatex code can look like this:

@Manual{ltc3600,
  title        = {{15V}, {1.5A} Synchronous Rail-to-Rail Single Resistor Step-Down Regulator},
  year         = {2011},
  number       = {LTC3600},
  note         = {Rev. D},
  organization = {Linear Technology},
  url          = {https://www.analog.com/media/en/technical-documentation/data-sheets/3600fd.pd},
}

And the output in IEEE style like this:

I believe this is similar to the Tex SE answer you linked.


While applying for PhD in mathematics, am I better off dropping a course I'm performing poorly in? I may or may not 'drop' my Point Set Topology course since I performed very poorly in it (also I am partially inclined to 'drop' the course so as to avoid a low GPA ),  but I do not have any idea whether it will be a problem during my PhD application or interview. I want to know how much it matters.

(I am the graduate coordinator of the math department at UGA.)

Point set topology is a good course to take for an undergraduate hoping to go to graduate school in mathematics, but it is not a required one for admission to any program that I know of.

If you are asking whether it looks better to have a transcript containing point set topology with a poor grade vs. a transcript not containing point set topology at all, I would say: if it doesn't appear at all, it looks much better.  Point set topology is an introduction to the abstraction / generality of graduate level mathematics, perhaps more so than any other undergraduate course because nowadays we don't really have separate undergraduate versus graduate offerings in the subject: the point set topology that you learn as an undergraduate is in many cases the point set topology that you take with you through your graduate career.  So doing well in that course looks like a good predictor of success in graduate level coursework.  Thus by doing badly in it, you're showing a clear data point which is not in your favor.

I should say though that dropping the course is not helping out your education or graduate preparedness at all: at best, it's papering over the problem.  Are you sure you can't redouble your efforts and actually do well in this important course?  Or the flip side of the same question: are you sure that you can truly drop it with no stigma whatsoever?  E.g., will it show up on your transcript as a withdrawn course?  Will faculty who write your letters know that you really struggled in this important course and responded by dropping it?  (If I were writing a letter for such a student and had this knowledge, I would probably feel honorbound to mention it.)  These are things to think carefully about and discuss with faculty advisors.


How to avoid personal issues interfering with my Ph.D. studies? Background

My life had (and still has) difficult problems (chronic disease and its consequences: social isolation, anxiety, having to take extra measures to take care of my health...). As a disclaimer, I am in psychological therapy right now.

That being said, I managed to finish my bachelor’s degree (mathematics, with a very uneven transcript with both very high and very low grades) and after that I did my master's degree, this time with very high grades. I did my master's thesis and then I was offered Ph.D. admission by the same research group in which I did the master's thesis.

To the point

Now, the problem is that I am constantly reevaluating if I should do my Ph.D or 1) either find another program or 2) move outside the academia, and I am unable to make this decision due to the fact that I think personal issues are interfering. I identify several more or less objective problems:

The Ph.D is in an applied department (biology), which is formed by specialists in the area, I have been told the research group is good in its area by a professor from another department (from the same university) which I trust, but since I am a newbie and have little knowledge about the area, I cannot test it myself. I have been here for 6 months.
I did not choose this Ph.D. They chose me. I think ph.D students are actually scarce where I live, and that is a reason why they hired me. I feel I could be in a more satisfying Ph.D program or area.
It is likely difficult for me to get funding in other Ph.D programs due to the problem stated above. This Ph.D is the comfort zone, I have funding (this is Spain, where even in private companies salaries, for graduate students with no experience, are not that high).
I feel like academia is a good fit because it allows me to work without a fixed schedule and in a non stressing environment. Note that I have not really worked outside academia.
My Ph.D supervisor is great, I like her as a person. But although she is a mathematician, I sometimes feel like she left mathematics and now she is a biologist more than a mathematician. This is leading me to think she not the right person to supervise a mathematician, past Ph.D students are happy with her, but most of them have biology related studies. Also,
She chose a Ph.D subject which is now in stagnation. I warned my Ph.D supervisor about this likely outcome several months ago and she told me to trust her, up to a point where we showed we could no longer apply the technique (a particular machine learning technique) we were trying to apply to the topic, leading to now being trying to publish a result so that the time employed is not lost. I now feel as if we were trying to desperately find something to publish from a technique that has proven not to be effective. Not to mention that reading through papers in biology is difficult, as I find myself not understanding lots of things.
I have enjoyed research, but not most of the time, I fear this has to do with personal problems not letting me concentrate, when I should be reading papers, I end up procrastinating with the phone or forums. I can concentrate when I like what I am doing, but not right now. I also do not feel like I have a strong passion for researching in a particular topic.
I am also concerned about overspecializing in a particular topic that leads me to not being hired afterwards.

So the question is, How can I make sure I want to pursue a ph.D?,  i. e.  pretty much what title says, I want to avoid personal issues interfering with the decision.

The problem is easier than it seems. Your first responsibility is to yourself. What you most need to do is find a way to build a life for yourself that you will enjoy over the long term. This could involve a PhD or not, however, you seem to really be attracted to Academia where a doctorate is required. Earning the degree might actually be the sacrifice you make to achieve your goal. But keep the "good life" rather than the PhD foremost as your goal.

A number of things in what you say indicate that you might profitably look at a different subfield than mathematical biology. You can use your current position, as well as your past education, as a base from which to conduct your search, speaking with other potential supervisors and even institutions. Don't underestimate the value of a good supervisor, of course, and she may be able to help you if you approach her in the right way.

Having been chosen, rather than choosing is possibly a big part of your problem. You "went along" at a point at which it might have been better to "take charge". This led to a number of your other dissatisfactions, I think. But you can take charge without making a clean break at this point until you see something that you really want to do.

Also, since you were chosen you should have confidence that your skills are not in doubt.


Can a graduate school change its decision, from rejection to acceptance? I have heard stories that graduate schools revoke their offers because of various reasons, such as lying in the application and some other serious misconducts.

As the title suggests, can the opposite happen? And can you recall any such events? If you have, why did they happen?

The typical process that happens in graduate application selection is that you subdivide applicants into three groups:

Accepted applications: These are your first choice of candidates, and you offer them a position. This list need not be ranked.
Waitlist: These are the applications you think are good enough to accept, but not so good that you want to make them an offer right away. You rank this list, and go top to bottom as people who have previously been offered a position decline.
Reject: These are the applications you think aren't good enough to succeed in the program. There is no need to rank these candidates because you don't anticipate ever tapping into this pool.

The point I'm trying to make is that the rejected applications are candidates you don't think are good enough to succeed in the program. You're probably going to ask what happens if everyone declines and you're out of names on your waitlist? Well, then you just don't make any more offers. That's because you've previously decided that the ones on the "Reject" list aren't good enough to succeed.

The only thing that's worse than not being able to fill all graduate student slots is to fill these slots with students who you believe will not succeed in the program. That's a sunk cost: you're paying for people who aren't good enough to do the job they're paid for.

So, under the assumption that all universities work on such a system (which I think is, in essence, the case), there is no reason ever to turn a rejection into an acceptance.

(Now, has it ever happened? Almost certainly, given just the sheer number of cases. Does it happen on a regular basis? No.)


Keep my promise not to work? Short version: a potentially upcoming 5-authors maths paper already have authorship dispute drama. I am not among the 5, but I believe I found the solution to the part they got stuck on. Should I tell them?

=====

Full story:

A is a new graduate student, never have any papers, decided to work with B a post-doc (who had a few paper before) on a topic. They did some significant amount of work, but still nowhere close to a result. A tells his friend C (another graduate student with 1 paper), not to ask for help but just to let him what kind of thing he is working on. But unexpectedly, C got interested and work on it for a while and get some new results, before telling A and B. The work is significant, but A claims that give it a few more weeks and they could have gotten that themselves. C's advisor come to his defense and insist that it is only fair that C got credits as well. Unfortunately, C asked D - an undergraduate in his junior year - to develop a computer simulation, which ended up allowing C to realize that a tweak to the hypothesis is necessary. C never told D what it is for, but D realized that after chitchat with B. Now D's advisor also want him on the paper, since he did some work, and it would really help him in applying to grad school if the paper can be done soon. Neither A nor B is happy about this of course. To make thing worse, E a post-doc from CS department, happen to see the problem and some work on the blackboard. He knew some papers in CS that happen to solve some aspect of the problem, and told B about it, which eventually cause their earlier work to be much more simplified. B thinks E deserve coauthorship too, much to A's resentment.

Now I was not there for the drama. A told me about this over beer a few months ago. I curiously asked what the problem is, and A told me everything including what they are stuck on, after making me promise to never work on this and never tell anyone else about the problem (A don't want to have a 6-author paper). I kept the promise, until recently, when reading some papers, and I found something and realized that this could be as well the key to the problem. After some more computation, I am 90% sure that this method would solve the problem, but I promised not to work on it, so I did not work out everything. Now I am torn. They are unlikely to ever know about this method (I'm in a very different specialty), but their effort had been walking in circle for a few months now. I had never had a paper before, so if I do put effort into this, at least I wanted to be recognized for it.

EDIT: as far as I know, there are only 10 people who know about the paper, us 6 and 4 professors who don't really care nor contribute; it is not public. I think A considered me his emotional crutch which is why he told me about it. The work is probably not good enough for publication, right now it consists of a bunch of different approaches and reformulation that all seemed very close to solve the problem, but did not.

It sounds to me like you're asking this question in the  hope that someone will come along and tell you to do the right thing. Well, here goes: you need to do the right thing and not break your promise.

Honestly, aside from the obvious moral and ethical argument (which by itself is already pretty compelling), even from a purely selfish and utilitarian point of view I don't see how anything good can come out of you surreptitiously solving the problem and then claiming your place as the sixth coauthor. Given that apparently this is not going to be a very major result or paper anyway, the only thing you will achieve is to make an already messy and acrimonious situation only more messy and acrimonious. Most likely the paper will never get written, and even if it does, everybody including you will come away with a bad taste and little to show for all their efforts and frustration other than a dubious coauthorship, whose value will be more than negated by the effects on the reputations of a few of the people involved, especially you for breaking an explicit promise.

With that said, I think there are two options that could potentially let you participate in the work and benefit from your idea without breaking any promises or acting dishonestly:

Tell A that you have an idea that may be used to attack the problem. Make it clear that you intend to honor your promise not to work on the problem, unless he gives you explicit permission to do so (until the 5-author paper is made public, at which time your promise becomes void anyway). At the same time, you are also under no obligation to tell him your idea, and may legitimately ask to be allowed to officially join the project (with the implied promise of coauthorship of the paper in the event that you end up making a meaningful contribution) as a condition of revealing your ideas to the group. They can then decide if that's something they're interested in or not - either way you are morally in the clear in my opinion.
In the event that you prefer not to discuss your idea with A and the rest of the group, or if you ask them to join the project and they refuse, you can simply wait until they make their results public, and then (as John Ma proposed in the comments) you will be free to use your methods to improve their results and publish your own paper, assuming your improvement is sufficiently interesting to make for a publishable result.

Finally, I should add that although six-author papers are relatively rare in pure math, there is nothing wrong or shameful about publishing such papers, and if each of the authors can indeed claim to have made a reasonably meaningful contribution, then the paper will still have positive value to everyone's careers. Furthermore, going from 5 to 6 coauthors does not involve such a major dilution of the credit (beyond the level to which it is already diluted), so if you have a genuinely new and useful idea to contribute that could make this a pretty attractive proposition for the existing coauthors.


How to tactfully refuse to be listed as a co-author I was involved with a research project with a group of people. Eventually, a paper was written and was accepted by a journal. I did not agree with the content of the paper and I didn't want to be listed as a co-author, but I did not want to create bad feelings between myself and the rest of the group.
The journal has a policy where every co-author has to turn in a signed agreement that they substantially contributed to the work. I refused to sign that document I thought that by refusing to sign that document, I would be left off the co-authors list.
However, the journal went ahead and included me without me signing the form. It is not going to ruin my reputation to have my name on this paper, but I didn't want my name on the paper and I am just wondering what I should have done differently to resolve this problem.

Well, there were a lot of mistaken decisions here, including by the journal to list your name.
To answer your title question, you should have, before the paper was submitted, told the group you didn't want to be involved, whether you said something like "I disagree with the conclusions of this paper, and don't want to be an author," or a white lie like "I don't feel I contributed enough to be an author."

You can also offer to be happy to be acknowledged; I find this is an option for authors who do not wish to hide an unwilling collaborator's contribution without making them co-responsible for the publication. This can soften the blow of disagreement without requiring to lie. - Captain Emacs

Upon getting the form, you shouldn't have silently declined, you should have talked with the other authors and informed the journal you were included accidentally. With everyone's consent, they would have taken  you off.


How to proceed if email asking for a recommendation letter hasn't gotten a response after 3 weeks (during COVID-19 pandemic)? I know this question has been asked before but here is the Covid-19 Pandemic Edition:

I emailed my professor asking if they would consider writing me a LOR almost a month ago and haven't gotten a response. Normally after a week or two I'd sent a follow up email but email etiquette seems a little different right now due to the Covid-19 pandemic.  I don't want to pester them or seem out of line but I would really like a response and am not sure what to do. I'm not sure if I should ask again because this professor is not planning on teaching next semester and I would imagine is busy working on other summer projects. And, like most of us right now, is probably trying to deal with the many uncertainties brought about by the Covid-19 pandemic. I did very well in the class but the last month of the semester was very disrupted due to coronavirus and all classes had to be made entirely virtual, so I'm not so sure that I'd stand out as a well-performing or memorable student as much as one would under normal circumstances, but I'm not sure who else to ask at this point. I need the LOR in the fall so the deadline is still several of months away. Should I wait another week or two and see if I get a response? Should I send a follow up email now or just assume that no reply means no?

Send a follow up email reiterating your request and the deadline. It's likely the professor has just lost your email in their inbox. It happens, pandemic or not.


I submitted an assignment late. Should I email the professor to explain my situation? I submitted an assignment 1 day late. Should I email the professor to explain my situation? He has a pretty loose policy on deadlines but we should definitely submit the assignment before the answer is posted.

I dropped the assignment in his mailbox so if he does not check on Sunday and posts the answer keys on the same day, he might be suspicious of me submitting the assignment after the answer key is posted, which is way less acceptable.

Tell him, he may or may not accept your submission, but if he wants to accept it and needs proof that it was before the answers came out then telling him provides that.

Don't provide a 16 page opera about why it was late, an apology, a (short) reason : medical etc and close.


Resolving dependency on a senior colleague before deadline (Disclaimer: While this question pertains to interpersonal issues in a working environment, I feel it is better suited to this SE than Workplace because of it being in the context of a lack of formal responsibility/hierarchy in an academic environment)

Background

The deadline for the camera-ready version of a publication and conference registration is in seven days; In order to finalize the publication, I need to specify information regarding licensing of the primary data (it is literally an HTML field which I have to fill out in order to submit the paper; It is not "just" some legal requirement).

I do not have the experience to decide what licensing is applicable to the type of data I need to license, the hosting setup for the data which will ultimately be in place, and the country/countries involved (I don't even know what countries would have jurisdiction). However, this would in theory not be a problem, because a tenured member of staff, who also said they would settle the issues regarding hosting, said they would "look into it". This person is otherwise not related to the publication or project.

Problem

The problem now is that I have been regularly asking the person who offered to deal with licensing issues about the status of the issue for two months now, with the frequency and seriousness of the contact increasing (first it was an occasional e-mail, then more e-mails, then physically finding said person and asking them in person). However, now I have almost no time left, and I still have no resolution to the issues described above.

I do not doubt the person's "trustworthiness" since they are a senior member of faculty in a small department and I know neither this department nor the person very well. Nevertheless, I cannot trust that this person will magically do what I asked of them one hour before the deadline because the stakes are too high. How can I resolve my dependency on this person before the hard deadline, i.e. how can I get them to do what they said they would do when I cannot do it myself and don't have time to find someone else who can? The person is neither my supervisor nor formally part of any project I am working on, so I have absolutely no way of formally resolving this.

Circumvent that person as a stop-gap. Specifically:

Fix a time shortly before the deadline when you are certain you'll be able to make the submission setting the information yourself.
Guesstimate to the best of your ability what the correct value for the licensing field should be - erring on the side of making data openly available (i.e. something like CC-BY-SA or public domain).
Regardless of how you communicate with the person who's supposed to take care of it - when the time comes, if s/he hasn't done it, you do it.

If that person gets upset after you've applied this stop-gap - you'll be able to argue that it was necessary so as not to miss the deadline, and that you're sorry but you had no choice. You can also say that if s/he believes somethinh else should have been put in, you both can always communicate the change to the organizers and say the original input was an error.

As for an effective way to "prod" this person, I can't suggest something particularly great without additional information, but:

You could talk to someone who's in close contact with him/her, and on speaking terms with you, and get them to try to apply their influence.
You could bring this up in a public forum where s/he is also participating, e.g. a group status meeting.
You could let him/her know about your stop gap in advance. That's a bit risky because they might object and you may end up having to directly disregard the objection.


PhD Application Review moved from Fall 2015 Start to Spring 2016 Start: Options? I applied for PhD programs starting from the fall of 2015 at several universities and I was waiting very eager to hear from them, because I just finished my MS last spring semester and don't want to waste time.

However, in the mid of this month, I received email from one school telling me that  the admissions committee has decided to move my application for review to the 2016 - Spring semester. In the letter, it didn't mention any acceptance/rejection. They moved my application for the next review which doesn't mean I have been accepted.

I'm really wondering why and how did they decided to delay my application. Moreover, What would you advise in this situation? Can I ask them the chances of acceptance or the reason for the delay? Or should I just wait?

I think given the amount that I know from your post, I don't think we can give you more advice than to ask for clarification.  This isn't a standard procedure that I know of, though I could imagine various reasons this could happen.  Since I'm just imagining (and I'm sure you are too), just write a polite email to the director of the graduate program, or whichever faculty member seems to be the correct contact, and ask them precisely what this means for you.


Author title(s) and affiliation(s) What is the correct format to fill out this field in a form? What if the author has no affiliations? Could you please give some examples?

  Author title(s) and affiliation(s):

Author: Ninja Cat

Title(s): Dr

Affiliation(s): None, unaffiliated, or https://academia.stackexchange.com/search?q=no+affiliation


Does auditing a course hold any value? I was quite curious about doing neuroscience so I reached out to a prof in my institution (IIT-K), who suggested me an online course on the same (MCB80). I have audit the course and am following it through, but recently I was wondering that apart from the knowledge gained, will I have any proof that I did audit this course and I have sufficient amount of knowledge about the course? Will I be able to use evidence of my audit (screenshots) as an evidence of completing the course? How am I expected to communicate with any one else that I have done this course properly?

This is an issue because purchasing the entire course on the edX site is not possible for me as I do not have the required financial support.

Many of your questions (about edX) will be answered by Wikipedia. Whether you can get transferrable credit for any course is up to the individual institution who might accept such a course or not. I don't know, but doubt, whether and institution would tell you before you take such a course whether it would "count".

However, it is a potentially viable resource for learning. But that depends on what  you actually do in the course, not just what you see or sign up for. If the course provides you sufficient practice and feedback on your efforts then you could learn something, even if you have no  visible "credit" for it.

Audits, even in face to face instruction can be valuable provided that the student does the work and gets appropriate feedback.

And even if you "purchase" an entire course there is no guarantee that a university will accept what you have done. It is pretty risky for certification, even if they issue "certificates". However, the sponsoring institutions (MIT and Harvard) are reputable, so there is likely less risk than if it were run by "Dr. Buffy's Online Diploma Mill". I'm pretty sure that one is disreputable.


Gaining software skills after PhD. Is it possible to achieve it in 6 months? I have completed my PhD few months back. I am about to start my postdoc at my PhD lab soon. I will be working on a different project than my PhD.
My PhD was in computational materials science. I used open-source finite element software to carry out my research. I had to write Matlab scripts for developing 3D geometries (used a bit of computational geometry: voronoi and a bit of topology optimization) and python scripts for postprocessing of the simulation results. The codes I wrote were quite basic.
Now, for the postdoc position, I have to implement machine learning algorithms in my research. I have 6 months before I need to provide tangible results. I need to learn and be proficient in python modules.
How to go ahead with learning the language proficiently and also learn the fundamentals of machine learning in 6 months. Is it possible to do so? Or I am tackling an impossible task? Any guidance with the course material?

Yes
Barring exceptional circumstances, this should be possible.
It depends, of course, on a few things:

How much time you have (e.g. hours per week that you can spend on studying)
What resources you have at your disposal (some courses are for free, others are not, and of course you need the appropriate hardware)
What is meant by "tangible results"
How fast you learn

There are many courses that teach machine learning in python. Here is a list of some courses, most have a duration of 3-6 weeks, with 3-8 hours per week. This blog entry lists free courses that focus on specific python libraries that are useful for machine learning. I checked the contents of a few, and they consisted of a series of video lectures, with only a couple of hours in total length.


'Teaching position or teaching oriented position' in Mathematics in Europe? Here I am talking about teaching positions in Mathematics. In the US, we know of many positions in the US (also in Canada?) that is teaching position or teaching-oriented position. For example

1) Some universities need 'regular postdocs' like other places, but they have more teaching load (3+2 courses or 3+3 courses for a year)

2)some are called 'teaching postdocs'.

3) Positions in liberal arts or community colleges etc.

Is there any such equivalent position in Europe? I have never been to Europe so far, but I hear there is no concept of 'colleges', they only have universities or institutes. I know there are some places in the UK where they advertise positions for teaching purposes. But for Europe minus UK, say Germany, Austria, Scandinevia etc. is there any such position for teaching at a higher level than high school (could be undergraduate courses, beginning masters courses etc.). Where can I find out about these positions? Mathjobs and euro-math-jobs don't seem to advertise them.

If you know any source for it, could you please provide me with the link(s)? And do you have any idea whether these positions are permanent or temporary and how much normally do they pay?

From my experience in the Netherlands, there are no teaching (oriented) positions on the university level (assistent prof level and up). There used to be an option to primarily be a teacher at university, without the requirement of a PhD even, but nowadays such a position always includes a mix of researching, teaching, and grant proposal writing.

At tradeschools (we call those HBO) there are (almost) full-time teaching positions, but these are not on a university level, and require you to speak fluent Dutch. This makes these kind of jobs hard to get for foreigners.


Acceptability of 'not possible' for master thesis research I am currently working on my (Computer Science) master thesis, and have been doing so now for about 5-6 months. The default time for thesis work at my university is about 9 months. Aspects such as literature review and 'planning' were all approved and went very smoothly.

Slowly I am realizing that the method I have planned to develop is simply not going to work. To be a bit more clear: I have thought of a model to solve a problem that isn't currently solved, and have found that my model doesn't improve on any of the exisiting 'attempted' models nor does it contribute anything 'useful' to the industry other than 'this model doesn't work.'

My question being: is it acceptable for the conclusion of a master thesis aiming to implement a new model for a problem to be 'doesn't work?'

I am worried sick that my supervisor or anyone evaluating the work would dismiss it, since of all three research questions I have not been able to make a single one work and have concluded bit-by-bit that it's not possible to implement this model effectively.

Is it acceptable to conclude a master thesis with 'my method doesn't work?' Will it be a problem if I just present my work and conclude with 'no success?'

You have to discuss this with your supervisor / advisor.

Then you can agree the direction.


Emphasizing in paper: italic or bold? I am preparing a paper for a conference in EECS field. (Generic suggestions are also welcomed!)

What should one do when he or she wants to emphasize one particular word?
Should that word to be emphasized be in italic or bold?

I myself rarely see text in bold, which makes me wonder whether bold should only be used in section or subsection headings?

It's a matter of style: both bold and italic are typographically acceptable ways to emphasize part of your text, as are small caps, use of a different typeface or point size. (And, if you wonder: no, underlining is not considered good practice in modern printed text).

However, you are right that using bold is usually reserved for headings or very strong emphasis (think things like “Danger of electrocution — do not use under water” is an appliance user’s manual). The reason it is not commonly used inside running text is that it disrupts the overall level of gray of the page, drawing the eye from too far away: you want emphasis to make certain words stand out in the sentence, but you do not want the reader to skip directly to these words as soon as they set their eyes on the page.

There are, of course, always exceptions… like when you use bold (or color, or any other device that disrupt the grayness of the text), to emphasize a few stand-alone “take home messages” from a longer text, as I've done above!