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What should I do when my supervisor wants to correct my thesis in parts instead of all at once?
I submitted my master’s thesis four months ago. And now, my supervisor sent me an email with only 10 pages corrected. She informed me that she will send me the feedback by parts!
I am really concerned about this as I am afraid references would all be in the wrong order when I join things together. One of her requirements was submitting the thesis as a whole and now she will correct it part by part after four months.
What should I do in this case?

Why would incorporating the feedback involving any kind of joining things together?

You say thanks and get to work incorporating the changes…

@TimRias because we will be working on different documents. She will not wait for me to adjust the parts she sent according to her feedback. However, she will keep adding feedback on a different document

References: use a reference manager. If it is text-based (LaTeX), also use version control, that's a good way of keeping track of different versions. I have no idea if Word has support for multiple version threads or there are tools for that.

Always keep in mind that the thesis is your work and responsibility, and not your supervisor's. Do your best with whatever you get, and don't demand anything that would imply it's her responsibility that your thesis turns out well. It isn't.

I don't understand how this is an issue. If you'd rather get the feedback all at once, wait until your advisor is completely finished to make any changes. Your advisor providing you incremental feedback is better than them waiting until the end, as it gives you the choice about how to proceed.

Very often subsequent revision of later parts depends on the details of the execution of revision of earlier parts. For example, when I comment to a PhD student something like "section 4 has to either do this,  or do that", then section 5's revisions may depend on which choice they make... so I'd prefer waiting to see their choice before giving further editorial advice...

From your supervisor's perspective they are probably doing you a favor: Since they are too busy/cannot manage to correct (or give feedback on) the thesis as a whole, they give it to you in parts whenever they have time.
What is important, however, is that you discuss with your supervisor what the planning/schedule is. Regardless of whether you have a fixed/hard deadline or not, try to make arrangements that work for both of you (and realise that even agreed upon deadlines may turn out to be flexible/need to be moved around when other priorities arise on your supervisor's end - your thesis is understandably and rightfully your main focus but your supervisor probably has tons of other things on their plate).
Ultimately, a supervisor typically also won't want to stretch out this phase on purpose, so communicate and try to sort out what your and their responsibilities and possibilities are here.
As for the references: I don't think this needs to be connected to when or how you receive feedback. As mentioned in another answer: Do yourself a favor and work with a reference manager from the start. It is a bit of a learning curve and hassle to get started if you've never done so, but you will never want to do it in any other way ever again. Your supervisor might advise you here as well.

I'd say don't worry too much. This is somewhat common practise. Keep in mind that your supervisor might be engaged in other tasks (research, proposal writting, teaching, ...) so sending you feedback bit by bit is beneficial for you as you receive it earlier and thus have more time to incorporate it. So the supervisor might act like this to do you a favor.
Also: I'd suggest using a reference manager (e.g. Zotero). References an their order should not be a problem when using a good software package taking care of your references.

I would be far more concerned by the four month delay than by the piecemeal nature of the feedback.
If there is an agreed-upon time frame for the completion of your thesis-- which means feedback, revisions, whatever presentations are necessary-- and the time-frame supports your schedule for graduation, great, fine.
If not, that is your real problem, not the niggling details of how you manage references, citations, etc.
The management of those niggling details is an annoying but very real fact of life, both in academia and in the rest of the world for people with higher academic training.  Your advisor shouldn't be (and almost certainly isn't) making life intentionally difficult for you in this regard; nevertheless, it's good experience to learn to deal with it.


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Alternatives to Word/LaTeX
I wrote my undergrad thesis using Word. Overall I managed to get the job done, but as with all WYSIWYG editors, by the end I feel like I had created a bit of a mess. It didn't show, but I know it's there.
Soon I'm going to start writing my postgrad thesis, and I'd like to use a tool that gives me more control over the single elements. The obvious choice seems to be LaTeX, but as I'm going to be writing a thesis about humanistic matters, I wouldn't be using all its advanced features. Also, it's something I don't know, and it would take me a while to learn how to use it properly.
But here's what I was thinking. I know HTML. I know CSS. (I used to be a web developer, as a matter of fact.) Is it possible to use them for typesetting in an effective way? I think about XML and how it may be used with a specific schema to declare semantic tags, but of course that would need either a converter or a very complex XSLT.
I'm not asking about a specific software, but rather the approach. I'm not sure if my end is worth learning a new tool, or even creating a new one.

Related: http://academia.stackexchange.com/q/17961/13404

I'm voting to close this question as off-topic because I think it should be migrated to Software Recommendations.SE

Note also that "I'd like to hear your opinions" isn't a good fit for Stack Exchange.

I hate to be that guy, but... I believe latex is still the way to go. In the sister website, TeX.SX, we have many users with non-engineer background that use latex for their documents. Regardless of your field, latex allows excellent document organization, referencing, and etc. If you feel uncomfortable with latex syntax, you can use Lyx that offers latex advantages but in a more user friendly fashion.

Depends on your field, but I would still use LaTeX. Why? It allows for automatic referencing of images/tables/etc., automatic page numbering, auto date formatting, easy headers, auto layout (as well as being able to set it yourself), automatic PDF rendering, etc. Plus it is super easy to work with git. It's such an amazing tool that on literally every document I can, I use it.

I will also recommend LaTeX, but don't install it locally. There are nice websites nowadays with LaTeX editors built in, and they even have lovely themes you can apply and start using immediately. I have used both ShareLaTeX and WriteLaTeX (now called Overleaf) with great success for about three years now. The added benefit is cloud-based backups of your document (still save everything locally, though)

since you won't be using its advanced features, I would recomend LaTeX. I don't think it would take one much time to get familiar with the basics of it. (the not so short introduction to LaTeX was really helpful to me!)

I second the point of view expressed by @sylvia: In a very simple case, i.e. not using any "advanced features" of LaTeX (whatever you define as such), you can just write your text while just needing an extremely minimal set of commands. Mind you, you don't even need any commands to separate paragraphs (a blank line will do), unlike what is the case in HTML.

Real authors use troff: http://www.cs.vu.nl/~ast/home/faq.html

HTML and CSS for typesetting?

No, just no. HTML and CSS were designed for looking good on screen, not paper. Although there are CSS styles for page printing, using a medium for a purpose other than its original design will at best be an overkill (if it works) and in most cases a utter source of frustration.

The obvious choice seems to be LaTeX... I wouldn't be using all its
  advanced features.

This is the basic misconception. You will use all advanced features even in a humanities thesis. You will have inline references, bibliography entries (bibtex), links to bibliographic entries, figures, title pages, perhaps even tables etc. As you see, you are going to use much of the advanced features of LaTeX, regardless of your discipline. LaTeX has an increased initial learning curve, but in the long run it is worth this time investment.
Conclusion: Stick to LaTeX.

I use LaTeX for a social science thesis (so no equations, etc). It took me about a day out of my whole three year PhD to learn enough LaTeX to get started, and it was well worth it. I use referencing, glossaries/acronyms, chapter and section labels and references, and tables and love that they're all formatted for me!

+1 I second LaTeX. At least try it out, if you have programming background it should not be too daunting to learn. The Not so short introduction to LaTeX is a good start. LaTeX and Friends by van Dongen provides a relatively recent introduction with very visually pleasing layout.

@Penguin_Knight I would recommend What are good learning resources for a LaTeX beginner? on TeX.SE.

Further, there are nice websites nowadays with LaTeX editors built in, and they even have lovely themes you can apply and start using immediately. I have used both ShareLaTeX and WriteLaTeX (now called Overleaf) with great success for about three years now. The added benefit is cloud-based backups of your document (still save everything locally, though).

I propose a second conclusion: stick to Word. Although the OP obviously can write code, it's not a science thesis. LaTeX is indeed great, but Word is still okay. Advisors who may not be very computer literate might appreciate its editing features. It's just a bad idea, when you're trying to chop down a tree, to toss out two of your sharpest axes and start slapping the trunk with a herring. Because that's probably what writing a thesis in markup language would feel like.

You dismiss HTML/CSS too easily; with the right setup it's perfectly capable of generating sensible printed documents.

@Phil Congrats, but I'm seriously skeptical about learning "enough" LaTeX in 1 day to write a thesis. I have been using TeX variants since the first year in uni (approx 11 years now) and I still often get stuck with stuff that I personally think that it should be straightforward to do. I don't know if you are some soft of a genius (or perhaps I am particularly slow) but I would strongly challenge the notion of LaTeX being "easy" to learn.

@posdef tex.sx is a very useful resource and a friendly community. For simple questions, people don't mind if you ask in chat. For more difficult questions, there is of course the main site. I don't think there are many people who understand TeX, but if you are not doing anything exotic, it is not hard to use.

@posdef He didn't say he learned enough to write a thesis in a day; he said he learned enough to get started in a day. As you do new things, you have to learn more, but it doesn't necessarily take that long for you to basically be able to write your text, while having a decent sense of where to look if you want to do something you don't know how to do. LaTeX really isn't very hard to learn; what's hard is mastering it.

@posdef It's very flattering to think so, but I don't think I'm a genius! For the same reason, I doubt you're 'slow'. I wouldn't suggest LaTeX is 'easy', but I was able to use a book template and get it building within one day, with a bibliography. I think that is achievable within a day or two, especially for someone familiar with markup (html). I then use http://en.wikibooks.org/wiki/LaTeX as a comprehensive resources for things I don't know.

Most university libraries have a LaTeX expert if you get stuck.  Don't roll your own thesis editor - use something where you can get help if you need it.

@cpast true, I guess my expectations are quite a bit beyond getting a chunk of text compiling. I usually set my baseline to getting as visually pleasing content as I can produce with InDesign, which is surprisingly difficult with LaTeX. I do miss that there is no WYSIWYG editor for LaTeX (ref: http://tex.stackexchange.com/q/144589/4271)

I personally would stick to LaTeX for a humanities thesis, but that's not a super useful answer. So,
Consider using Markdown + pandoc to generate LaTeX, while being easier to write. Something like this workflow for a Linguistics dissertation: http://linguisticmystic.com/2015/03/04/how-to-write-a-dissertation-in-latex-using-markdown/
pandoc is a powerful conversion tool, allowing you to convert between markdown, LaTeX, Word, etc. It will potentially even allow you to embed certain HTML and/or LaTeX elements into the markdown, giving you occasional access to either domain as necessary.

Regardless of the subject at hand keep in mind that you are writing a long document, that is destined to print and thus you need tools to tailor such a document. Use the right tool for the job!
MS Word and it's spin-offs (Apple's Pages, OpenOffice Writer etc) are good for quickly whipping up a document, where getting the content right is the primary focus.
For good quality publishing you need more than that, which explains the domain which (La)TeX was designed to operate in. If you need a WYSIWYG editor that gives you much better control over individual elements than Word does, you can test Adobe InDesign or similar software. The concept is that you prepare your content elsewhere and design how the content is going to be displayed, then you plug-in your text in the placeholders which you have styled.
The issue here is the learning curve. No matter if you go with (La)TeX or InDesign or anything similar to that (Scribus is a free alternative for instance), it will take you a significant amount of time to get down and dirty with the software and to be able to create exactly what you want to do. So the question is; what would you rather spend your time on, learning how to make a more visually appealing thesis, or generate the content of the thesis.
TLDR:

If you know how to use LaTeX already than just stick with it, despite not needing the "math".
If you have never used LaTeX and feel like you want your WYSIWYG features, give a try to InDesign.
If you have no time to learn something new then stick with whatever you know and generate the content instead.

Though in theory HTML+CSS can be used to typeset page based material (e.g. http://www.w3.org/Talks/2013/0604-CSS-Tokyo/), I would stick with some other format that can be easily be converted to HTML (I personally use LaTeX for lengthy documents, Markdown for short ones). If you are good at programming, and willing to invest in developing tools to convert your own HTML format to other formats later (it is likely going to be necessary), then go for it. You can be the leader!
The main disadvantage of HTML is the lack of semantic tags. Perhaps an XML with some good XSLT that spits out HTML is better for organization.

That's kind of what I've been thinking about. I think that XML would actually work great for this kind of stuff, maybe with a schema that includes semantic tags and whatnot. A XSLT or even a plain converter to LaTeX could be very useful in this kind of situation.

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Will I need to retake my written qualifying exams?
I am early in the process of considering whether or not to return to a PhD program in mathematics.  The program consists of three parts:  a set of written exams (usually after year 1), an oral exam focused around the topics of one's specialty (year 2 or 3), and finally a thesis/thesis defense (year 4 or 5).
I stopped this process before the second and third parts, but in the process I earned an MS in Mathematics.  I'd like to return to my original university and finish what I started, but I'm hoping to avoid retaking the written examinations.
There are two arguments.
First, that I be required to retake the written exams:  It's been nearly a decade (that's right!) since I took them last, it would be justified re-preparation, and I would learn a lot by studying again to pass them.  After all, 9 years is a long time and the previous results shouldn't 'last' that long.
Second, the I be allowed to pick up where I left off.  Nine years is a long time, but it should be considered to be an extended leave of absence.  I have already proven myself as capable of passing these tests, as evidenced by my MS from the same university.  While I couldn't pass the present day equivalent versions of these exams (without much study), neither could many graduate students like me who are further along in their study.  Also, supposing such tests have expiry, what would the year-length of the expiry be?  Wouldn't any choice of length for an expiry be fairly arbitrary?
Given this situation, what can I expect?  Are there known universities with and without expiry on such tests?
What would be equitable here?  Most importantly what arguments can be made that I retake and pass these exams despite pertinent regulation?

Usually, rules for qualifying exams are a matter of official department policy, and should be in writing somewhere.  If your department's policy says the exams expire, then they do.  If it doesn't say, I would say you could make a good case that they do not expire.

Alternatively, if they don't say, then they can make a good case that they do. And even the department agrees that they don't expire, you might not be able to convince someone to be your advisor unless you retake them.

I am no longer planning on returning to graduate school.  But in hindsight I agree with the answer below.  Regardless of a policy argument that exams results don't lapse, any sane PhD advisor should require a retake.

I don't think this is a matter of finding the right argument.  If the rules of the specific department in question explicitly allow you to keep your old exam results upon readmission to the program after any length of time, then if you are readmitted to the program then you get to keep your old exam results.  If they explicitly say the contrary, then you can't.
More likely there are no explicit rules which address what happens when a student leaves the program and then is later readmitted to it: there are no such rules in my program, for instance.  In this case it is really up to the discretion of the faculty in the department.  Moreover, since this issue will probably be bound up with your admission decision, being too pushy or argumentative about it could really backfire.  I would say that it is certainly fair to explicitly mention that you passed the written qualifying exams in the same department at such and such a time, that you are excited to pick up where you left off, and that you are wondering whether this will be possible.
I do not advise that you say more than that or that you give detailed arguments.  It is also not a matter of "equitability": they are not obligated to admit you at all, so they cannot be obligated to admit you with certain additional constraints.  To be honest, the harder you argue about this the more strongly I suspect that you should take the exams again.  Nine years is more than long enough to forget everything that you learned as a master's student.  Indeed you write

While I couldn't pass the present day equivalent versions of these exams (without much study).

Since you have said this, I would seriously consider removing the identifying information from your account!  Your own confidence that you could not pass the current versions of the exams is rather strong evidence that you should take them again.

neither could many graduate students like me who are further along in their study.

There are almost certainly no other students in the program who passed their exams nine years ago.  Many PhD programs have total time limitations for exactly this reason.  (There was a student in my program who had done everything but his thesis, and he eventually got timed out because he had taken too long: I believe, after nine years.)  Moreover the other students in the program have spent the time since the exam pursuing their studies and going on to fulfill other programmatic requirements.  You left the program.  It is really not an equivalent situation.
Note also that although the mathematical knowledge tested on qualifying exams is not really time-dependent, nine years is long enough for the structure of the exams to have changed: if e.g. the syllabi are different or if the faculty know that the standards have been raised, then it is probably inappropriate to hold a newly admitted student to different requirements than other newly admitted students.  To be fair, it is probably more likely that there has not been significant change.
Finally, let me note that you haven't mentioned why you are not willing to take the exams again.  According to the structure you described, if you retook all the coursework it would take only one more year.  One year in an American PhD program is not a long time at all.  Taking one year to solidify your foundations before proceeding could actually allow you to graduate in less time than if you dive into something headfirst and then only after months or years realize that you need to backtrack to learn the basics.  Please think carefully about whether getting excused from the exams is really in your long term best interest.
Good luck.

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What happens if I get a grant for my PhD project but I decide to quit and start another PhD somewhere else?
My PhD is self-funded and I probably change in 2016. Can I apply for a grant now and then change my PhD later?
Does it legally or ethically force me to stay and finish the project?
Or my Prof can hire another PhD student after me to continue working on that project?
UPDATE
Let's put it this way: I have a self funded position and everybody including my Prof know that I'll most probably leave next year but I want to get some money as long as I'm here (before changing).
Can I apply for a for example 2-year research grant and quit after a year or it obligates me to stay and finish the project?

It depends on the terms of the specific grant, so you'll have to read them.  Of course you can't be forced to work on the project (unless slavery is legal in your country) but if you change topics or institutions, you may or may not be able to keep the rest of the money.

" Of course you can't be forced to work on the project"... that's exactly the question. If I apply for a grant and get it I'll have a contract to finish the project. Isn't it like that? and if so, I'll not be able to quit and leave my current university. +1 btw!

Contracts don't work like that - what you describe would be, maybe not slavery, but at least indentured servitude.  A grant contract would more likely say something like "You work on this project, and we pay you."  If you stop working on it, you stop getting paid - that's all.  Worst case, you might have to return some of the money you were previously paid.  They're not going to throw you in jail.

Of course you are not forced, nor obliged, to finish your current project.
But it might very well be that, depending on your grant conditions, you will have to pay back the grant money, fully or partially, when you do not finish the project.
Furthermore, it might be wise to try to keep being on good terms with your current professor and colleagues. This is most likely achieved by involving them in your decision and motivations (which you might already be doing, as you write in your question).

You are self funded, so you have no employment contract. You can quit at any time. However, this might not look so good on your CV. You should try and avoid burning bridges with your current supervisor, since a good reference from them would be beneficial.

The question is about whether accepting the grant involves any obligation, which you haven't addressed.

@ff524 Actually I think I'm a little confused by the question. My interpretation was that the grant was specifically at another university.

@ff524 is right MJffryes! I want to know if getting a grant legally prevents me from changing my university and getting another PhD position


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What is the difference between CV and Resume?
I'm applying to some schools in US for my PhD studies. In the application form there are two different places for uploading CV and Resume.
What is the difference between them?

Don't downvote, help.

This is a place to share information. If someone downvotes it would be friendlier to explain at least :)

I haven't down voted, but I cannot see how this question is specific to academia (or why the OP hasn't just googled the answer), so others may well feel the same.

Thanks for comment @Jessica. According to your argument I think 60-70 percent of questions in stackexchange can be googled but having a comprehensive subjective discussion where people oppose and agree each other with different perspectives is the point behind stackexchange (I could be simply wrong but this is my impression). In my case yes, if you simply google you can find tones of articles about this question but you can not connect them in anyway. Some say they are the same and some say they are different for some reasons. Asking such question here may help people in application process.

Resume is shorter.
Resume provides a summary of your education, previous employment, skills, and accomplishments.
CV  includes all the above in more details plus publications, presentations, detailed teaching experience, etc.

Thank you :) I'll wait for more responses and in the case I do not get more info I'll accept yours. Thanks again!

A resume tends to be a one page summary of what your background is with respect to the position you are applying for. informally needs to be a very dumbed-down version of what you have done so it can be understood by a general audience specially the early-stage selection committees(read departments staff, random volunteers, HR personnel).
In a resume, a person might focus on marketing his accomplishments in a context of the positions this person is submitting the resume to.
CV or curriculum vitae can go any number of pages and can include all aspects of you. I have heard people having 150 pages of CV and using them for applying to positions like a program manager at NIH with 40 pages for their publications alone.
A CV is in a way an absolute document and you don't edit it based on the position you submit your CV to. It is generally all there is to it.

It may be worth pointing out that the distinction made between resumes and CVs in the other answers is very much a US thing. Outside of the US, things are different. In Europe (including the UK) resumes are uncommon. Instead, CVs are used for everything. However, CVs are generally expected to be much shorter. As a rule of thumb, a European should fit on two pages A4, and is tailored to the job you are applying for. Also, unless specifically requested, a European CV will not contain a list of publications. European CVs thus fall somewhere in the middle ground of American resumes and CVs.

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Math postdoc: 'endowed' postdoc vs. ordinary postdoc
It seems that most of the job advertisements for postdoc positions in mathematics (in the US) on mathjobs are named positions (I.e. "Smith visiting assistant professor" or something like that), and each department offers a very small number of such positions (usually between 1 to 4). But when I check the webpages of the relevant departments, it seems that they usually have something like 20-30 postdoctoral researchers, and most of them are not named postdocs.

What is the common way to apply for those ordinary postdoc positions? (I can't find them on mathjobs and I know that math postdocs in the US are usually not hired by a PI)
When you apply for a named position on mathjobs, does it mean that the hiring committee will automatically consider you also for the ordinary postdoc position?

There are some postdocs funded by grants, where the hire is in fact made by the PI(s) (maybe with open applications, maybe not).  There are also self-funded postdocs (NSF and similar) where the postdoc has their own funding from a grant or fellowship, and more or less just has to call up a faculty member and say "I would like to come and work there"; you won't see applications for their positions either.

But it is generally true that any postdoc position for which there is a "search" (i.e. anyone can apply) will be advertised on MathJobs.  So the ones that aren't there need not really concern you.

Having 20-30 postdocs at one school is rare.  What schools did you see that at?

I hope it is not true that most mathjobs postdoc ads are for "named postdocs", because these exist mostly at very elite places: I am not aware of more than a dozen of them altogether.  Generally there are on the order of 100 postdocs you can apply for at mathjobs.
Anyway, mathjobs is quite good at separating out various kinds of positions.  At my institution we currently have four different mathjobs ads including two different kinds of postdocs.  Of course you need to read carefully and understand the differences among all these positions (not all of our applicants do so).

I can go in and look if you insist (really; it's not a problem), but I strongly suspect that if you are only finding "named postdocs" then you are not reading carefully enough and missing the other postdocs offered by the same departments.  The vast majority of math postdocs really are available on mathjobs; I can't even think of another place that advertises these positions in any quantity.
It is possible, but I wouldn't assume so.  Again, mathjobs allows you to tailor your application to multiple positions at the same place.  For instance you may use the same recommendation letters but submit different cover letters, and you might submit a teaching statement for one position and not another.

If you have any questions about this, feel free to ask at the department of interest.
Added: Thanks to Noah Snyder, who pointed out that the "dozen" above is not accurate.  In fact that is the answer to a different question, namely how many departments offer postdocs under the name "Firstname Lastname Assistant Professor".  I would be interested to know which departments offer "named postdocs" of one kind or another.  I think it is more than 12 and less than 50.
Actually, I just checked, and 36 out of the top 55 math departments have named postdocs.  So probably there are about 50 altogether.

My department considers all postdoc applications together and decides separately which candidates should be offered the named positions (Hildebradt research assistant professor, which has a lower teaching load) and which should be offered regular postdoc positions.

Indeed, I just took a quick look, and found that mathjobs currently has 166 positions under type "Postdoctoral", mostly with generic titles like "Postdoctoral research fellow" or "Research associate" --- not to mention 124 non tenure-track faculty positions, some of which are possibly appropriate for a postdoctoral position.

I think you're mistaken about the number of "named postdocs."  I can think of a lot more than a dozen off the top of my head.  It's the norm among say the top 50 schools to have an "X assistant professor" position, where X is some professor from 50+ years ago.

For some examples: Berkeley, Stanford, UCLA, UCSD, Caltech, Illinois, Michigan, Minnesotta, Northwestern, Chicago, Indiana, Purdue, Ohio State (two different names!), Johns Hopkins, Maryland, Texas, North Carolina...

@Noah: Clearly I am at least somewhat mistaken.  I actually tried to look this up a while ago and didn't get as many names as you did.  I don't think there are anywhere near 50 though (or I am still mistaken).  Wait: I remember now.  I was looking specifically for "Firstname Lastname Assistant Professor" postdocs, because the Assistant Professor is taken seriously by some departments when it comes to credit towards tenure.  Thus for instance Texas has an "R H Bing Instructor", which doesn't count.

I didn't mean to say it got as high as fifty, only that it was a clear majority of the top 50.  It's also a bit quirky who does and doesn't have them, and as you say whether it's instructor or assistant professor.  At Columbia the Assistant Professor was taken very seriously, as Ritt Assistant Professors have access to Columbia faculty housing, but postdocs do not.

My experience of applying for post-docs through Mathjobs was that the named positions and the un-named ones were often advertised and applied for together. Sometimes different eligibility criteria apply (for example some positions are only open to those who already have a connection with the US). Where there were different positions, a lot of ads would say 'applicants for X will also be considered for Y'. A few said 'applicants for X will not automatically be considered for Y'.


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What to do after I was named as co-author on a paper, without my consent?
A few years ago, I found a conference paper, of which I was a co-author, being re-published as an article in a (not highly ranked) journal. It was a textbook example of plagiarism, copy-pasting whole sections and using figures from our paper, which the author did not even cite. The author came from a foreign country and was an associate professor.
My co-author wrote an email to the author who committed plagiarism and asked him for an explanation. After a while, he admitted his misconduct (a soft version of it); his explanation was that he forgot to cite our paper. As a settlement, he suggested a new article to be written together with us. Without consent, he wrote a new article (in his language) and published it in another lowly ranked journal, putting himself as first author. I was listed as a co-author without being able to comment on the idea of writing this paper and without approving its content before publication. For me, it looked pretty much like a (self-)plagiarism---a translation of the original English paper. But, at that time, we did not want to further complicate this weird situation.
My question is how to deal now with this article I co-authored and could even be treated as a kind of self-plagiarism. May/should/must I exclude it from my CV?

PS. The publication of the journal we are talking about stopped five years ago. So, it is not possible to contact the editor any more.

If the journal in question stopped publication 5 years ago, why have you waited so long to do something about it? Have you just discovered that it happened?

Who was the publisher of the journal? Are they still in business?

what country does this person happen to be from?  It sounds like he's trying to legitimize himself and cover over his former plagiarism off of your work.  I'd contact his place of employment / university.

@easymoden00b: I think the country this person is coming from should not be blamed for his behavior. And yes, with his second paper, he made things even worse. However, I am not asking how to get him into troubles so many years later. (He is a family man now). The only dilemma I had was: to accept or reject the authorship of this paper ...

Your final question is a bit confusing. Do you mean that you at one point did have this copied paper on your CV? Or are you asking whether you should remove the original paper that this person plagiarised from your CV?

@Lilienthal: Of course, I am asking whether to own or disown the translated paper. If I accept the authorship, this would be a self-plagiarism and the way to legalize the first plagiarism. If I deny the authorship, I would in some way say that the ideas presented are not mine ...

Given that the publisher shut the journal down five years ago, there's not much more you can do at this point to have it removed or retracted at the source.
Instead, I would disown/disavow the paper by not including it in your CV and by making sure it is de-listed from your Google Scholar, Web of Sciences, SCOPUS, and other author citation index listing services.
This is easy in Google Scholar by deleting the article from your page in your author-view. For SCOPUS you have to send a correction request:

http://help.scopus.com/Content/h_authorfeedback.htm

The same for Web of Science

http://ip-science.thomsonreuters.com/techsupport/datachange/

For both Scopus and WoS, I would list the correction that you are not the same person as the author listed as the author of the paper. It would help communications with both services if you know your author identifier (Scopus Author Identifier, etc.). Something like the following should work:

Dear SCOPUS -
My author identifier is _________. One of the articles listed in my
  author record (____________ in the journal ________; DOI _________)
  was not written by me. Please delist it from my author record.
John Doe (Author identifier ________).

p.s. This is actually not an uncommon problem. Adding Famous Authors® to subpar articles is a form of authorial pagerank-jacking.

is it possible to contact places like SCOPUS and WOS to request it not to be indexed with the reasons?

Info added to answer.

As I mentioned, the journal was lowly reputed. So, the article is listed neither in Scopus nor in WoS. However, your suggestion for corrections would be useful.

If it's not in your CV and not indexed  any where that matters, then I really wouldn't worry about it.

The author agreement of the journals I know usually state that all authors must have given explicit approval before submission. It seems like this person did not respect that requirement.
This is a valid reason for you to write to the editorial board and request retraction. Since you say that the journal got discontinued, if the publisher is still in business, they might be able to assist.
Not putting the duplicated article on your CV is a good idea, but not enough to protect yourself from the behavior. Especially if the article contains plagiarism. You don't want to be associated with that.

The publication of the journal we are talking about stopped five years ago.

@Alex: You need to add this very pertinent piece of information to the original question.

"Not putting in on your CV is a good idea" - I think the OP is referring to the original (legitimate) article in their inquiry on whether to exclude it from the CV, while I'm not sure whether you are referring to the original or the involuntary article here. (In case you mean the original article, I wonder whether it can really be so easy to sabotage someone else's work, by simply publishing what looks like self-plagiarism in the original author's name, thereby forcing the original author to exclude the legitimate works from their CV?)

@O.R.Mapper I meant the duplicated one. There is nothing wrong with the original one. I edited my answer to remove that ambiguity.

Of course, one problem is that many journals only require agreement forms to be signed by the first author.

@Kimball That doesn't mean the journal is not asking that all authors explicitly approved the submission.

I know, but it means journals often trust the lead author to deal honestly with the other authors and there is nothing done on the part of the journal to pre-emptively prevent situations like the OP's.  Some journals, such as American Mathematical Society journals, do require explicit permission in writing from each author.  OTOH this causes some hassle when one author is no longer alive or is a former student that is difficult to contact.


text:
Found and corrected a mistake on someone's else paper -- praxis?
3-4 months ago I found a big mistake in a theorem's proof of a paper that had just been published (stats/cs community). I immediately notified the authors, who claimed to be aware of the mistake but admitted not knowing how to fix it.
This week I noticed that the authors didn't notify the community of the mistake (no updates on their arxiv paper, nothing on the author's websites, etc), even though the paper got a good visibility and already has quite a few citations. I went to check whether the mistake breaks the theorem, and found out that:
1 - the theorem statement is incorrect
2 - however, the statement holds under reasonable assumptions, but the proof requires a different technique (I have a full proof for that)
How should I proceed in this case? This theorem specifically is the main result of the paper, so I wouldn't be comfortable just sending them the corrected proof+statement and not getting properly recognized (e.g. only an acknowledgement). Should I write a short report on post it on arxiv? Send the proof under the requirement that I be added as a co-author? What is the praxis in this case?
EDIT:
Thanks for all the replies so far. I've contacted my advisor and have finished writing a short report (~4 pages) on the matter. I'm yet to decide whether I should contact the authors before posting the report, and whether a merger would be a good idea -- as some people said, it is a huge red flag that they admitted to be aware of the mistake and never took action, so I am not certain that I would like to be associated with the authors in a collaboration level.
1) The paper has already been published in a conference and its journal proceedings. I've contacted the authors after acceptance but before the proceedings were published, and they took no action.
2) The paper proposes a new algorithm B and states that it has the same running time as algorithm A, a classic method in the field, while having smaller memory cost and being easy to implement. The main theorem roughly states that T(B) = O(T(A)), which is wrong as for some inputs T(B) = infinity. The technical mistake is subtle and involves upper-bounding an infinite series, which in reality can diverge to infinity. I've ran the algorithm on a simple instance where it does not halt nor makes any progress.
3) It can be shown that, for some input distributions, T(B) = O(T(A)). The technical argument is a bit different from what they initially presented. Unfortunately, I think that for these distributions it can be shown that A has an even smaller memory cost than B, but I am not sure.

When you say "published", do you mean just a preprint or in a journal?

It's been published in a conference and proceedings.

I suppose that my vocabulary is not all that good because when I saw "praxis" in the subject, I didn't know what was meant, and all I could think of were the well known teacher certification tests that U.S. (public) school teachers have to take! (I actually took this test back in 1988, when it was called the NTE test.)

@DaveLRenfro You're not alone, I was expecting some kind of philosophical essay about learning through practice...

While I agree that answers should mention the praxis, I believe that you're really after the correct response and not merely imitating the crowd?

Are you able to find a problem instance for which the proof evidently fails?

Yes, I have a simple instance where the theorem statement/proof does not hold at all (in any weak sense).
In a very high level, the theorem is on the running time of an algorithm, and I found one instance where the algorithm does not halt. Under mild assumptions on the input it can be shown that the asymptotic running time is the same as what they originally claimed.

All I can say is the following: I wasted about half a year because of a wrong paper I used to build/code an algorithm. Hence, please do whatever is necessary to make false statements/theories being removed.

Edited: OP mentioned that the paper has been published in conference (not just on ArXiv as was implied by phrasing).
This can be tricky.
The main thing is that you get due credit, and that the scientific community is aware of the mistake.
If the mistake is in a key theorem that is the basis for the entire paper, then the authors should retract the paper. As unpleasant as this may be, it is the only ethical and fair thing to do. If the authors aren't doing this of their own volition, then you should (I would argue that you are in fact ethically obliged to do so!):

Make 100% sure that you are right in your claim. It's not entirely clear whether the theorem is "dead in the water" i.e. the claim is demonstrably false (via a counterexample that you've constructed), or that the claim might be true, but their proof is wrong, and you couldn't have come up with a proof either. If you have a counterexample, then you've really managed to kill the paper.
Otherwise it's still an open problem (not a bad thing, just a different scenario).
Consulting with your advisor/senior member of the community, contact the conference program chairs to inform them of the mistake. You should let them know where the paper is wrong; you can also mention that you have a correction that you're keeping to yourself, but you are not obliged to provide it. A paper with a wrong proof should never appear in any conference.

This paper should not be published. If it was published in a high-profile ML conference (ICML/NeurIPS/COLT etc.), then the organizers can be trusted to take it from there.
You must understand though that if you go and report this to the organizers without the authors being on board, then this may result in some unpleasant interactions with them in the future (to be fair, the authors brought this upon themselves by not owning up, but still - people have egos and pride).
Next up, The ideal scenario is that you email the authors, the authors agree to working with you, adopt your proof technique and add you as a coauthor (assuming you’ve corrected an important theorem).
This is assuming that the authors are being reasonable and don’t have a fragile ego.
To make that happen you’ll need to phrase the email carefully “I think that I can show that Theorem 1 holds under some minor assumptions, can you take a look at my proof? I’d love to collaborate with you on a future version of the paper” or something like that.
If that doesn’t happen (radio silence after you email them or worse: them saying they had already thought of this idea or some other nonsense), you need to get your credit somehow (e.g. by getting a supervisor/mentor to intervene). You could concurrently start writing your own version and upload to ArXiv (so it’s publicly timestamped), referencing the original.
Most cases I’ve seen followed a merger of authors but there are some distasteful instances, be prepared and be pleasant and you’ll be fine!

This sounds good, but I think language like "explore this further" is needlessly ambiguous.  I'd use phrasing like "I would be happy to collaborate with you on a new version of the paper", which makes it clear that you are expecting co-authorship.  I would also be inclined to withhold details of the correction from the initial email, until they show interest in working with you.

Absolutely. Also if this is a student asking, their advisor should know about it ASAP and offer advice/support

@Spark If you think Nate's suggestions are good, you might want to edit some of those into the answer, so that they are not hidden in the comments.

If they are aware that the main result of their published paper is wrong, and they haven't retracted it, is a red flag for me.

@Davidmh, I see in the comments that the OP mentioned that the paper has already gone through peer review and published. This makes things much more serious than an ArXiv upload.

"A paper with a wrong proof should never appear in any conference." A paper with a wrong proof should never be submitted to any conference. But -- guess what? -- people make mistakes. That includes referees and authors.

@DavidRicherby of course they do. They should also admit them, and make sure that the community is aware. That's the ethical thing to do.

I think "take a look at my proof" is a bad idea. The next thing you may hear something like "oh oh it's our idea, we are sending (just sent) it to a publisher" or, better, you'll just find your results in their paper after a while without any feedback and will have very vague opportunity to stand your ground. I would advise to put the result to arxiv.org or, at least, private github before continue any conversation even with your own advisor. Academician and scientific advisors are crooks not less than in any other human activity - believe me, first hands.

I have not been in such situation. However, I do not see what is the problem for you to write a paper complementing or correcting the previous paper? Isn't this how science works?
In my opinion, it is very toxic culture in academia to consider such thing as inappropriate. Those previous auathors are humans. Assuming good faith, that was what they knew and what was to the best of their knowledge, and to the best of the reviewers' knowledge, at the time of their publication. You got something, great, you have the right to get the credit for it. I feel it is unfair that you inform the authors offline. You should write it, and publish it too.
Also, we should not forget that you might be wrong too! I do not mean to offend anyone. But if we remember that we are all humans and have limitations in our knowledge about exploring this world, we would take these issues in a more relaxed way.

Some issues: 1. The paper has already been published - it can't stay published 2. Just a standalone proof of a theorem is often difficult to publish on its own

@Spark maybe it's my physics background speaking (or general science/stats “all models are wrong, some are useful”), but IMO the issues you're listing aren't issues. It'll be a new theorem, one that actually has a correct proof, whereas the original paper presented an incorrect theorem, but nevertheless was valuable as the starting point. The original paper should by all means stay published, it just needs an errandum added.

Any good faith assumption goes out the window when they agree there is an error, have had months to retract their paper, have not notified anyone, and continue to reap the publicity and impact. You don't let information you know is wrong propagate like that when you're responsible for creating it.

There are two separate issues here in my opinion - the first is the mistake found in the original. That cannot stay uncorrected (who'll remember some email exchange 10 years from now? Someone needs to know). The second issue is authorship/credit for new result.

@Spark You can publish it in repositories not necessarily in a conference or paper. As a researchers, if I will build seriously on a pepr's theorems, I normally look at the others who cited it, and have a look. If I found a paper with a title that corrects something, I will look at it. But if we ask any paper with any flaw to be redacted, I think only handful of papers will remain! All papers are peoples' ideas from their own perspective of the world, and it can be flawed.

@Spark There i a responsibility of a new author to build on something is to track what happened and wrote about it.

@user9371654 I completely agree. I am simply saying that it is also the authors' responsibility to publicly note errors in their manuscripts. If they don't do it - the community should. There is a difference between "a perspective is flawed" which sounds subjective, and passing off a falsified theorem as true when it's been proven false.

If you think that you have found the proof of a paper, or you can (with evidence) notify the community of the wrong/improper/incomplete proof, you can itself publish it as a research article.
For example, look at the following comment (to be) published in IEEE Trans. on Wireless Communication: Comments on “Coverage Analysis of Multiuser Visible Light Communication Networks”
Linke can be found here

Colaborating with the original author makes your paper less controversial, saves the face of the original author, and might lead to a faster scientific consensus.

(From comments from the OP)

In a very high level, the theorem is on the running time of an algorithm, and I found one instance where the algorithm does not halt. Under mild assumptions on the input it can be shown that the asymptotic running time is the same as what they originally claimed.

As someone who works on statistical algorithms, it's my opinion that you found an error in their paper, but I have trouble believing this is the entirety of their paper; I've never read a paper that just proves "Such and such algorithm is O(nk)" and that's it. But I could be mistaken; perhaps this paper was supposed to provide a proof that a well known heurestic algorithm actually had strong theoretical backing?
Anyways, if this false claim was not the entirety of their work, I would guess that their paper is not so broken that it should be retracted, especially since it sounds like you've come up with a set of conditions for which the statement will hold true. But it is worth noting the mistake and correcting it within the academic literature. As such, I would guess that it is very reasonable to write a follow up paper to the journal that essentially says something like the following:

In the paper Previous Work, it was stated that the algorithm  would display quadratic convergence under conditions X. We demonstrate that, in fact, the algorithm can fail to converge under X and Z, and provide the necessary conditions X and Y for which the quadratic convergence rate is recovered.

If the authors of Previous Work did not help you write up this new paper, there is no need to share authorship with them, although it was very polite of you it point it out to them.
In fact, as a very relevant example, the very famous 1977 EM algorithm paper had an issue in its convergence proof, which was corrected in 1983 by a different author.

To be more precise, the paper starts from a classic algorithm (call it A), and proposes a variant (algorithm B). It then states that B has the same run-time as A, while having a smaller memory cost and being easier to implement.
However, for some input distributions B does not even halt, while A does: there is no upper bound on B's running time. I've now checked experimentally that this is indeed the case: there is a simple instance where the algorithm never makes any progress and thus never halts.

@komusou: above anything else, I would recommend talking to your advisor, as you can really get into the important details with them.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
How much emphasis should be put on programming ability in a letter of recommendation for CS graduate studies?
I have a little computer science background.  In one of my statistic courses we did significant data analysis with SAS and R coding.  I'm getting this professor to write a LOR for me.  He wants me to draft the LOR for him.
How should I write this letter so I don't come across as someone who confuses computer science with coding?
In other words, I want to convey how this course makes me a good candidate for a computer science graduate program.

I've followed the last LOR questions and from them I've learned it is probably a warning sign that he wants you to draft the LOR. See this related question.

Computer science ≠ programming, as you indicated. So focus on what computer science you know.

@MarkFantini Not necessarily - see What does it mean if a professor asks you to draft his reference letter for you?

Let us put aside the question of you vs. your letter writer, because the same question could apply to your statement of purpose.
The notion of "programming ability" per se is a bit of a red herring.  The problem is: how do you actually measure what a "good" programmer is?  The only meaningful answer that I know of is to illustrate what somebody has accomplished that required programming.  Any other assertion of programming ability, I regard as a red flag.  In fact, one of my litmus tests for whether somebody is a good programmer is to ask what languages they know: if they can readily and simply answer the question, they probably aren't a good programmer (the logic behind this: getting significant things done with software tends to, over time, involve being forced to deal with lots of random types of code in order to get things done).
Thus, I would recommend you instead focus on emphasizing what you have accomplished using programming.  This way, you will show not just "programming ability" but also personal organization, ability to accomplish difficult and complex tasks, ability to think in a computer science manner about problems, and other such qualities that actually matter a lot more for demonstrating your potential in a program.  If you did a cool and complex project in the class, that is what you should be talking about, with SAS and R being mentioned in passing as the tools you used.  If all you did was problem sets, well, I would have a hard time seeing how this could be a strong LOR in the first place.

"one of my litmus tests for whether somebody is a good programmer is to ask what languages they know: if they can readily and simply answer the question, they probably aren't a good programmer" - That's an interesting take. I am not sure if I agree, though.


text:
Will getting rejected from a graduate school affect my relationships with the faculty members at that school?
I recently got rejected from quite a few grad schools.  Will this make it harder for the faculty at these schools to take me seriously if I interact with them?
In particular, I'm interested in having a particular professor at the school I will be attending as my advisor.  However, until a few month ago, he was a professor at a school I got rejected from.  I'm afraid that he will refuse to work with me, knowing that I wasn't qualified for the graduate program he was recently at.

From personal experience: Not if you do we'll at the school where you were admitted.  I have a couple of joint papers with my would-have-been advisor at one of the schools that rejected me.

It is very unlikely that anyone who reviewed your application came away with a very strong opinion of you.  It's almost always, "meh, not so sure about this one...that one's a safer bet".  Prove them wrong and they'll forget all about your earlier application.  (Actually, most of them probably already have forgotten.)

I find it hard for this answer to not be:

It depends on the professor.

However, do not listen to the imposter syndrome. Focus on your accomplishments, not on perceived failures. So you did not get into program A. The reasons you did not get into program A could be anyone of a long list of unknown and unknowable factors that have nothing to do with you. The number of applicants to program A may be so large that you were not excluded because you were not up to snuff but because you were one of a large number of very qualified applicants. Maybe the chair or members of the admissions committee are lazy, negligent, or perhaps just overwhelmed - and thus your application was not even examined. Perhaps your application was lost by the bureaucrats before it ever got from the dean's office to program A. Think no more about program A.
You did get in to program B. Focus on success at program B. The professor you wanted to work with moved from program A to program B, so there must be something better about program B. Consider the fact that you were accepted at program B and that the professor you want to work moved from program A to program B to be indicators of a good chance of success at program B.
The only way that working with this professor will not happen is if you don't pursue it. If you worry yourself out of trying, you guarantee your failure.
Unless that professor was on the admissions committee, there is a fair chance that professor may not have even be aware of your application to the other program. Even if that professor was on the committee, he or she may not even remember you. Depending on the school and/or discipline, there may be hundreds of applicants. Even if that professor knew about your previous application, he or she should be professional enough to ignore it. If not, then maybe you don't want to work for that person.

I got hired as a postdoc at a university which had rejected my graduate application.
And not just that. When I started, I realized that they had made the right decision. The background level of the first-years was uniformly and substantially higher than what mine had been when I started. I would have gotten creamed.
You will have the opportunity to prove yourself in your first year. Do a good job, and any weaknesses in your application will be quickly forgotten.
Good luck!

It could work in your favor. You and the professor have a "common enemy."
The professor left Program A, and certainly for a reason. Maybe Program A was "narrow-minded" about the professor, and about you.
The professor landed at Program B, which had the "good sense" to also select you. The latter probably works more in your favor than the rejection from Program A is likely to hurt.

Or maybe the professor went to Program B because his wife got a job in that city, or because that city has better schools for his special needs child, or because Program B offered him a higher salary that Program A was not able to match. It seems... not very useful to speculate about why the professor switched schools.

@ff524: If Program B offered a higher salary, that was their "good sense." The other things you mentioned are possible, of course. But a family would give a higher priority to a professor's career than to that of a "random" wife or husband.

Who says the professor's wife isn't also a professor? (And who says that the professor's career necessarily gets priority? Some people might object to the characterization of all other jobs as being "random" professions having lower priority than the academic career.) There's just a whole lot of total speculation going on here...

@ff524: I said that a professor would have a better paying job than a "random" spouse. That can be established "statistically" by polling all the professors and their spouses (and there probably are some published statistics). Of course, the spouse may have a higher paying job or receive higher priority among any GIVEN couple. The ;point was I was trying to make was that a professor would probably out earn a "random" spouse. I (a man) would probably "pull up stakes" for a professor wife.


text:
Is it dishonest to start a PhD with intent to leave it and transfer to another program?
I just finished my master and am about to start a funded PhD in October. Thing is that the reason I am doing this PhD is because I missed the deadlines for the PhDs that I am truly interested in. I was thinking that it would be better to do something academic this year and get paid than to do nothing until the next application cycle.

Is this extremely dishonest to start a PhD without the intention to finish it? Will this put me on some blacklist at that university?
How should I portray my position in my other PhD applications? I was thinking of saying that I was doing a funded student placement program or something?

Doing a PhD is not equivalent to working at the food court, it's not something to do meanwhile you are figuring things out. If you have no interest in pursuing research on a particular field (i.e. can't see yourself finishing the degree) then you should probably not be taking that position. It might just be someone else's dream opportunity, as Charles Morisset mentioned, research funding is like water on Arrakis, you just don't waste it.

There are really no perfect endings to this situation.

Note that you will commit to some rules when you sign a contract, if you leave it, they can make start a juridical process, as you don't give any output and take some funding from them.

@optimalcontrol: Where does the OP say they are not going to produce any output during the time for which they stay there?

If you were applying to do a PhD in my lab, and you were currently in your first year of a PhD in another lab, you'd need a very compelling explanation to convince me that you were a serious & reliable student. And "I just wanted something to do for a few months while I waited for a better opportunity" would not cut it.

@O.R.Mapper I suppose that OP will stay just one year in this program, as he intends to apply in another program year after. I am not sure if it is possible to have some outputs in first year of PhD, which is not evident at all in fields as economics for example.

@optimalcontrol: This may be true for research results and publications, but depending on the project, there may be loads of other tasks that form a part of the work as well and that have to be started right away.

This is very field-specific. Labs are much more possessive about their PhD students rather than, say, a math department where the first year is coursework anyways.

For some of us, being able to research as a paid job is one of the best that could happen, for some others its just another job. No matter what advise you will get from here, your decision will depends on the way you look at it. From the spirit of your question, I think that you will fall into the later with an extra intention that you would be able to contribute to academia.

Is this extremely dishonest to start a PhD without the intention to finish it? Will this put me on some blacklist at that university?

That will depend on the nature of project and the type of contract you would be signing. For me, the contract is for 3 years and the project was designed such that there would be experiments and analysis during these years. As you would expect, the first few months would be learning the necessary tools and methods. If I chose to quit, that will put the whole project in jeopardy and if the group decides to take another candidate for the same project, unless they couldn't find someone who already know the tools to work, it is a huge waste of productive time.

How should I portray my position in my other PhD applications? I was thinking of saying that I was doing a funded student placement program or something?

Well, I suggest you tell the truth. Everything else would be trying to suppress what you had been doing. You really don't like the whole idea, do you?
You will never know if the person who would have been accepted instead could contribute more than you will in your proposed year, is passionate, or just another candidate looking for just another job. I am sure that you got the position because you are eligible for it, and you are in full liberty to choose what you do with it, but as I mentioned the first part, an ethical dilemma occurs when your intentions could jeopardize the whole project.


text:
How to overcome discouragement on finding major error in work just before paper submission?
Recently, we were working on a new idea in physics and engineering. At the end (after writing almost 6 drafts and even ready to submit draft) only I realized that I had made a stupid error in calculation and some errors crept up from numerical simulations. So it changed one main result which was the crux of the paper. Now the new result is not appealing as well. I explained the situation to my advisor over email and I am yet to meet him.
I am feeling so hopeless and diffident in the work. Has anyone else faced similar situation? How did you overcome?
We spent about an average of 4 months on this. Do you think I wasted a lot of time of my advisor on this and how can I try not to make errors in the future?

yea, it was just a small change in the code. it was subtle though. however, mistake is a mistake-whether subtle or not. Yes the results are very different.

The only guaranteed way "not to make errors" is "never try to do anything" - but that isn't good advice for anything in life. A good strategy is to just do something else for a few days (catch up with emails, grade papers if you are a TA, whatever....) and let your subconscious figure out what to try next. When you wake up one morning thinking "hey, maybe we could try XYZ ..." then get back to work on you research. A bad strategy is to sit doing nothing, while feeling "hopeless and diffident." There is always something that needs to be done, so get on and do it.

There are different things which trigger a burnout for a PhD student. It happens at least once for most people. It's very important to realize that this may be what's going on in your doctoral studies just now. As I say, it happens to most of us.

I found a (seemingly) major, stupid, and unfixable error in my PhD thesis about three weeks before the deposit deadline, but after the defense. (See http://jeffe.cs.illinois.edu/pubs/oops.html .) After I could breathe again, I took out the 15 tainted pages, added a footnote describing the error in the two published papers that chapter was based on, and shipped it off to my committee with the footnote highlighted. They just laughed and signed the approval form.

I think that the best way to overcome your situation is to realize that nothing out of ordinary happened - to err is human. In my opinion, research is about discovering truth and enriching knowledge (including the one of the researchers'). And making mistakes is a natural part of the process. I would just discuss my work with advisor openly and do my best to learn from that.
I don't think that you have wasted anybody's time. I (along with many other people) believe that negative results are also valuable (for example, see this paper, this journal and this workshop). The same applies to other results, such as similar to existing results, not impressive results, etc.
As for strategies for not making mistakes, I don't think there are any to prevent them completely, as I said, but you can reduce their probability by not advancing too fast in your research (i.e., rushing to obtain results or to publish) as well as asking feedback on your work from other people beyond your advisor or other people closely involved in the research (perhaps, even, from other disciplines, in order to obtain opinions, based on different perspectives).

hmm. thanks. i was just feeling so lost and hence I posted to see whether anyone else faced the situation. Thanks for the tips

@Vaidyanathan: You're very welcome and good luck!

You are correct, but keep in mind that "negative results" is not the same as "wrong result due to software bug". The former can and should indeed be publishable, while I see absolutely no reason to publish the latter.

@xLeitix: I have mentioned negative results and potential errors in connection with the "wasting time" aspect, which the OP has been concerned about, not the publishing aspect.

Recently, we were working on a new idea in physics and engineering.

I take it that "we" means you and your thesis advisor?

At the end (after writing almost 6 drafts and even ready to submit draft) only I realized that I had made a stupid error in calculation and some errors crept up from numerical simulations.

It's good that you caught it before submission and great that you caught it before publication.

So it changed one main result which was the crux of the paper. Now the new result is not appealing as well.

Do you mean that it is not as appealing as the result you thought you had, or that in addition to having to change the statement of the main result, the new main result is not appealing?  Anyway, "appealing" isn't really the standard here.  Are you left with a novel, publishable result or not?  That's something to talk to your advisor about.

I explained the situation to my advisor over email and I am yet to meet him.

Okay.  Of course you definitely need to meet with him.  Now for your questions:

1) I am feeling so hopeless and diffident in the work. Has anyone else faced similar situation? How did you overcome?

Yes, so far as I know the majority of working academics have faced similar situations to yours (I, as a PhD student, spent substantial time on work that turned out to be faulty).  How to overcome is personal, but very broadly: first you diagnose the situation to see what can be salvaged: very often it's something, and you have to work yourself through the psychological hurdle that what you actually have is "not as appealing" as what you thought you had.  But almost the entirety of published academic results are not what the academics really wanted to do: that's the way research works.  If you thought you had something and you are sure you don't (but first make sure: a lot of times a mistake turns out to be fixable, and stupid mistakes can be just as fixable as "smart mistakes", sometimes more so), then after a suitable mourning period, kiss it goodbye: there is no point comparing your present situation to the lovely situation that turns out to be counterfactual.  Then you pick yourself up and get back to work.  It is good to try to identify specific morals.  If you can learn anything from your mistakes, then you will have made clear progress, all in all.

2) We spent about an average of 4 months on this. Do you think I wasted a lot of time of my advisor on this and how can I try not to make errors in the future?

You haven't described the nature of your advisor's involvement.  If he was not involved at all, then I don't see how you could possibly have wasted his time.  If he was involved, then you're in this together, and it's his problem and his mistake as well as yours.  Every thesis advisor knows that all researchers are fallible and novice researchers are, on average, more fallible than veterans.  A faculty member who writes a paper with a student and just assumes that the student's work is correct knows that he's taking a gamble.
I think this is a good opportunity for reflection in your research process, both individually and together with your advisor.  Were each of you assuming that the other person was double-checking the work?  That's something to talk about and avoid in the future.  More specifics of how to do better in the future -- if there is some dramatic improvement to be made; maybe you just got unlucky -- are exactly what your advisor should help you with.  This is worth at least one lengthy conversation.  If what you hope to do better in the near future is not clear in your own mind, it may be worth multiple conversations and multiple check-ins regarding your future work over time.
Good luck.

Both previous answers are excellent. First of all, you should be very happy that you discovered the error BEFORE publication. Errors do happen, but you should also acknowledge the fact that there probably was an error in your workflow. Speaking from a CS prespective, for any new problem introduced in a paper, one must clearly describe what are the expected results when solving this specific problem. AFTER clearly defining the problem and its expected results then you must try to find a method that tackles it.
For this reason, many CS papers propose a naive benchmark method for any new problem, which might be super slow but it guarantees 100% that it gives
out correct results. Then the same papers propose an "optimized" version that does the same thing, but in a much better optimized way. Although in the paper the selling point will be the fast method (and how faster it is compared to the naive version), during the "debugging" phase the naive version is very important because it always gives you the correct results. That means that everytime you do an optimization, parallelization or something similar to your proposed method, you must always check that you get the exact same results with the naive method. That guarantees that no errors are introduced at any phase of your implementation which leads to a better paper, safer assumptions and correct results.
Speaking from a personal perspective, even after publishing more than a handful  papers and having done the coding in most of them (and knowing by now what usually works and what not), any time I add a new major optimization that makes the "optimized" version, e.g. 10% better, I almost always get a subtle mistake in the results (that might give wrong results in 3 out of 1000 experiments). Again, these errors are easy to spot because after any major code change, I always compare results to the naive method. Then I fix the "subtle" bug, get the correct results in all cases and move-on to the next optimization. Rinse, repeat.
Regarding your case, for a bug to go unnoticed for 6 months, it is obvious that you probably had no "naive" method that gave you the correct results to compare your results with. This is a big mistake that is mostly done by inexperienced researchers. We all should have confidence in our abilities to think of a better method to tackle problems in our area of expertise but thinking a correct idea and actually implementing it to be correct are two different things. You should always have a naive method that 100% gives correct results, before starting working on your "optimized" method. This slows down experiments (because the naive method might require several hours to run for all your test cases) but it is your safety belt when you are speeding towards a better solution. That way, errors are easily spotted within a few days or weeks and are easy to fix, many weeks before even starting to work on the paper. Moreover, it shows early if the method you thought has any true potential, before losing another six months to make it better. Also, it makes you confident that what you write in your paper is correct and no unpleasant surprises will happen, when other people try to recreate your results. Thus, next time, when you will work on another problem and a new paper always write a slow, bullet-proof method that gives correct results (checked manually one-by-one) before writing the new optimized version. The will protect you from losing another six months or publishing a paper based on wrong assumptions.

Very good point! Note though that the advice about a "naive" method does not generalize to other fields and is quite specific to CS algorithm development. However, for anything involving code, a viable alternative is the following: Implement everything in 2 languages (or even better, but slightly less realistic: have someone else implement it in a 2nd language independently). If the results match, then this reduces the probability of a careless error/typo.

You would be astonished at the amount of time that can be flushed down the toilet during your PhD. However, you have to remember something important: that's what research is. People get some strange idea that we always know ahead of time how an experiment will turn out. Nonsense, of course. If we knew what would happen ahead of time, it wouldn't be an experiment, would it? And every time one of those million dollar instruments goes down and you find yourself on your knees, elbows-deep in the cockles of random, unlabeled wiring, sometimes for months at a time.
Your PhD is not for you to show off how amazing you are to the community. Some folks have everything go right. Others don't. That doesn't make the person who had everything go right have a more valid PhD than the other. Ultimately, your PhD is about making the statement that you are a master in a particular field, and acknowledged as such by your peers. That doesn't always mean success. Sometimes it means tackling a problem in such a way as to make it easier for the next person ahead of you.
We all want to be the person who invents an FTL drive, cures cancer, whatever. Few of us will be. Few will become rich, famous, or particularly successful as a result of our research. That's how it is, and eventually (usually after the PhD) you learn to accept that.
It's hard to say what's best, to write more papers with less content, or fewer that were more powerful. The second would seem obvious, but it isn't. I knew a woman who got her PhD with something like 23 papers during that time. Which sounds amazing, and you would think she would be fast tracked for greatness. And you would be correct, her career continues in the Ivys, last I heard. But other folks sneered, since her papers consisted of "I changed the pH to 6 and wrote a paper" and "I changed the pH to 7 and wrote a paper" and you get the drift. But despite such sneering, she succeeds. So which is better? Clearly, hers was an example of a degree in which lots of things went "right." So what?
Your validity as a scientist has never been and will never be about your results. A PhD doesn't mean you are smarter than anybody. It means you had the luxury to dedicate the time to a degree. What a PhD is supposed to mean is that you have shown yourself trained in a particular manner of thinking. That you don't just plug the numbers in and read off the result. As a PhD, your job is to be he person who squints at the numbers, mutters under your breath, and tears the work even when it is your own and it tells you exactly what you want to hear to shreds.
So, for what it is worth, rejoice! Your post convinces me of nothing but that you are in a good place. Of course, it isn't easy to do-confronting an advisor with a screw up, facing this stuff, it's terrifying, humiliating, painful. But that's what it is to be a PhD, to have that ability to do all of this and walk through that fire, having the integrity to see what is true and what isn't, having the courage to pick your course, and having the strength to go through all of the slings and arrows that come at you when you go along that path.

Some statements are contradictory: "Your PhD is not for you to show off how amazing you are to the community" but "your PhD is about making the statement that you are a master in a particular field, and acknowledged as such by your peers"

I agree with @Alexandros that some things you said were contradictory. But I would just like to emphasize something you said. "A PhD doesn't mean you are smarter than anybody. What a PhD is supposed to mean is that you have shown yourself trained in a particular manner of thinking." Good point for OP to take home from this.

Showing off and making a statement are two different things. It is the difference between confidence and hubris.

@Alexandros I have a hard time believing someone has mastered something if they haven't botched a few things along the way. ;) I think that the distinct Broklynite is trying to make is that you're not there to show off how you're great and perfect and everything you do is gold; you're there to show you've done the hard, messy work of learning your field and how to approach new problems in it. Although perhaps it could be worded better.

Here are my lessons-learned from a firm where a "mistake-hunting" culture is strong. You can save a lot of time and have a clear mind on more important things, if you simply know or follow these simple rules:

Errors are human. The more you work, the more you make mistakes. Yet more disturbing: the smarter you are, the more you will make mistakes.
Ergonomy, structure and good practice will help you avoid making mistakes. For example using an appropriate code editor, write comments, etc. Don't hesitate to write a plan for your code, as you would do for a document, this will help to keep the overview.
Most mistakes are discovered during a first check, less are discovered during a second check, etc. So, it's inefficient to check again and again, just do a job as clean as possible from the start, and one or two thorough checks.
Check your methods and your results in a systematic and structured way: list of strategic things to check, what is expected, what is obtained.
If possible, show your work to someone else (or even have someone do a test independently). It's amazing how fast one can find one's own mistakes, simply by explaining, because it requires to set the mind in a different way.

"Yet more disturbing: the smarter you are, the more you will make mistakes." Can you explain this part?

@mmitchell - Perhaps Carine meant: the more projects you get involved in, and the more ambitious your projects, the more room for error.

I had the same experience: on the night before my Master's thesis defense, I discovered a mistake in my statistical analysis that completely reversed the result of the paper.  Worse, it reversed the result from something that was "interesting" to something that was only merely a confirmation of what we might already have suspected.  I spent the entire night correcting the mistake and reprinting all of the hand-out copies of the paper, and walked into the defense with only a couple of hours of sleep.
I have rarely felt more discouraged and disappointed in myself, especially because the mistake was such a simple one.
The professors had reviewed my previous incorrect drafts beforehand, so right at the start of the defense I pointed out the mistake in the previous version and how it reversed the results.  In addition to professors I knew from my department, they had also invited a statistician from the Math department, and as I described my mistake I could see him nodding.
During the QA at the end of the defense, one of my professors, who had always somewhat intimidated me, said (paraphrase from memory): "You could easily have simply ignored this mistake, walked in here and defended the opposite result, and none of us would have known any better.  I appreciate your honesty."  There's no way to know whether what she said is true -- for all I know, that statistics professor came in having noticed the mistake and ready to tear the results apart -- but her attitude about the issue certainly helped set my mind at ease about it.
They granted the degree.
You haven't wasted your advisor's time.  If your result is true, then it is still important whether it is appealing or not.  As far as avoiding errors: you'll never avoid all errors, but my own experience did leave me an important tool that has helped me to find errors earlier rather than later in the years since.  Whenever I start to feel that a result or explanation is "good enough", but I haven't proven it yet, though the urge is to move on and build on that result, I just have to think back to that all-nighter and the defense the next day to muster the motivation to check my inputs, assumptions and conclusions one more time.


text:
Unclear whether professor will provide assistance after sending through CV and an essay
I am a graduate civil engineer and want to pursue to academic career in the same field. I have been in contact with a professor, entirely by email, since March and he is responding me since March very well. I want assistantship to pursue my MS degree from the professor.
About a month ago he told me to write an essay about a topic and also to attach my latest CV. I have written an essay and send it to him. Its been a month and I have not received a reply.
He have sent me his last email on September 21 stating that he has received my essay. Now I don't know whether he has decided to provide me an assistantship or not.
Now I don't know how to follow up whether he is willing to provide me an assistantship.


text:
When and why should you write numbers less than 10 in words?
My current strategy in my thesis and in papers is to write numbers less than 10 in words. For example, if I need to write the number 6, I write 'six'. Recently though, I've been thinking this strategy adds unnecessary complications. Is there any particular reason why numbers less than 10 should be written in words?

Maybe the answers on the question when to write numbers with words on the German Stack Exchange are of some help to you (do not worry, you do not need to know German). I may turn this into an answer later, but won’t be angry, if anybody else does.

This is more a question for ELL.SE or ELU.SE I think, since it is not specific to scientific writing, and it depends on the language used.

@tohecz It's not specific to science writing, but it is specified by some academic style guides (APA for example), so I think it's on topic here.

The official rule in Spanish is to use the form that best improves readability (that is quite subjective). So, you would use "a million" instead of 1 000 000 (but 10^6 could be better). The rest is kind of fuzzy.

@KennyLJ: I learned to write numbers up to twelve in words, which arguably makes sense in my language, as twelve is the highest number (for a long while, anyway) that sounds like a proper word rather than derived and combined from other numbers. Analogously, I suspect the guideline(s) in English are not completely arbitrary, either, even though their rationale might not be quite obvious.

Many style manuals will have specific requirements regarding when to express a number as numerals or in words.
APA Style
The APA version 6 style manual has an entire section starting from 4.31 on how to present numbers in text.
The manual does include a general rule that numbers 10 and above should be expressed as numerals, but there are a wide range of exceptions.
Should be expressed as numerals

Any number in an abstract
Any number immediately preceding a unit of measurement  (e.g. 3 kg)
Any number representing a statistical operation (e.g., 4 times as many people)
times, dates, ages, scores, and points, exact quantities of money, numerals as numerals
numbers that indicate a place in a series (e.g., page numbers, Table 1, Table 2, etc.)

There are also further exceptions for when numbers should be expressed in words

Numbers at the start of a sentence or heading
Common fractions
Where the usage is common

Why have the general rule to express numbers below 10 in words?
Compare the following:

I had 2 apples.
I had two apples.

and

I had one-hundred and thirty-two choices.
I had 132 choices

A few possible principles:

Numbers less than 10 are easy to read as words; Large numbers are difficult to read as words and easy to read as numerals.
In general, conventions make life easier for writer and reader.
There may be a slight disruption to the look of and reading of prose when interspersing numbers with words.
A few numbers, particularly "1" (which looks a bit like "I") might introduce slight ambiguities.

I have been taught, without justification, to write integer positive numbers from 1 up to and including 10 in words, and all the rest using numerical symbols.
But this was a general rule, when I was learning my (native) language as a youngster. Nowadays, a sub-rule also appears somehow fitting to my eyes: the distinction between whether the number is used to count the cardinality of a collection, or to inform about the magnitude of an entity.
I would write "Let two firms engaged in oligopolistic competition" rather than "let 2 firms..."  (this is "counting the cardinality of a collection"). I would also write "Let fifty firms..." rather then "Let 50 firms"
But, I would write "the value of this parameter is set to 6", rather than "... is set to six".

But I suppose you would not write "In 2010 in the US, five hundred ninety-six thousand five hundred seventy-nine people died of heart disease."

@Nate Elderidge No, because here another criterion becomes dominant: the (avoidance of gross in)efficiency in transmitting information. With language, very rarely one can find black and white rules.

The school that taught me the rule you cite had an additional rule you left out: Once you have jumped to numerals, stick with numerals for the rest of the sentence or paragraph.

The last sentence is correct because "6" represents a measurement, and in general, those should always be written with numbers, not words.

@trickly A sensible rule, since as the reader reads (s)he also records meta-inforation on the writing style. It is also important (and wise) to note that the rule you state is non-symmetric.

@aeismail Certainly. "Measurements" is the efficient way to say it, instead of my unnecessarily twisted "magnitude of an entity".

@AlecosPapadopoulos I like "determination of enormousness, or lack thereof, of an entity's magnitudinality"

@OllieFord That's more like it!

I write numbers as words when they are used descriptively as part of the narrative, and with digits when they related to technical constants.
For example: I have two conclusions to make. First, the coefficient of 1 for reward is the best one to use. Second, the algorithm takes 34 iterations to converge.
But that's just my personal rule.

what do you mean by reward of 1 is best one to use?

@EnthusiasticStudent read it as "a reward coefficient of exactly 1.0 gives the best results"

Follow the style guide and the authors' guide for your publication. Almost all such guides for academic publications include guidelines for spelling, presenting numbers, and referencing.
If in doubt, quickly scan through recent documents from the same series (so, if it's a thesis, flick through some recent theses; if it's a paper in a particular journal, have a quick read of that journal), and reproduce their formatting.
After that, if still in doubt, ask your supervisor, advisor or editor.

I don't think it is an unnecessary complication, as long as you remember to be consistent. But it may not necessarily be appropriate for the document you're writing.
In English non-technical contexts, the style is often to write out numbers from one to nine as words, and then from 10 onwards as numbers (this is style used by the Associated Press and in some cases the Chicago Manual of Style). The Chicago Manual of Style also alternatively prescribes writing out numbers from one to one hundred as words. In both style guides, numbers that begin a sentence should be written out as words and Chicago specifies that, if this is an unwieldy solution, then the sentence can be rephrased. The webpage Numbers: Spell Out or Use Numerals? (Number Style 101) provides a useful guide, which goes into greater detail on good style for numbers in general documents.
However, you're likely writing a technical document, and as such, the guidelines are different, suggesting that numbers should be written with digits much more, with David A. McMurrey, author of Processes in Technical Writing, stating: 'You should use numerals, not words, when the number is a key value, an exact measurement value, or both. For example, in the sentence “Our computer backup system uses 4 mm tape” the numeral is in order.' The webpage which was the source of this quote provides helpful guidelines.

It depends. I'd for example say "the two above equations show that the value is 5". If it is a number (result, a quantity like "5 [km]", ...) I use numerals; for "narrative" numbers up to ten or so, like "two equations" or "nine people", even up to twenty, I'd write them out. If the number comes from an equation or will be used in one, or be somehow be combined with others, I'd prefer numerals. But also consider that text is to be read as text, not as a mixture of text, symbols, and numbers. In case of doubt, write it out. Even "a thousand repetitions of the experiment...".
In absence of some style guide (or red pencil by your advisor), don't worry too much.