I think a similar question was answered in PhD Research under guide/advisor of a different department by JeffE. I’m asking a slightly different question. Yet if you find it identical to the above one, you can report it to be ‘duplicate.’

The deadline of math PhD programs I’m applying for is approaching and passing, but I guess asking this question isn’t too late. I’m interested in both pure math and machine learning, and I chose to apply to math PhD exclusively due to my background. However, recently I’ve been into developing an algorithm related to proof verification and others for application to mathematics using the state-of-the-art deep learning techniques. I’m certain most or all professors in math department I’m applying to (or anywhere else) aren’t working in such an approach, since that’s more of a job of professors in CS department. Though the answer may be case-by-case, is this kind of topic appropriate for my PhD thesis? I understand that I can have a co-adviser from CS dept., but is this topic likely to get accepted from math department? If so, by any chance if my interest totally shifts to machine learning proper and has nothing to do with mathematics at the late stage of my PhD, do I still have to work on something related to math in this sense? (I assume the answer is Yes.)

The deadline of math PhD programs I’m applying for is approaching and passing, but I guess asking this question isn’t too late. I’m interested in both pure math and machine learning, and I chose to apply to math PhD exclusively due to my background. However, recently I’ve been into developing an algorithm related to proof verification and others for application to mathematics using the state-of-the-art deep learning techniques. I’m certain most or all professors in math department I’m applying to (or anywhere else) aren’t working in such an approach, since that’s more of a job of professors in CS department. Though the answer may be case-by-case, is this kind of topic appropriate for my PhD thesis? I understand that I can have a co-adviser from CS dept., but is this topic likely to get accepted from math department? If so, by any chance if my interest totally shifts to machine learning proper and has nothing to do with mathematics at the late stage of my PhD, do I still have to work on something related to math in this sense? (I assume the answer is Yes.)

I’m a 3rd year Ph.D. student in an engineering discipline, and I realize that I am better at starting projects than closing projects. By closing, I mean that the work gets published either in a good journal or a top conference.
I currently have 3 projects on the side (each of them started as a course project or a fun side project) apart from my thesis work. My advisor is flexible with this, but at the same time he would prefer me to invest all of my time in my thesis work (which is completely reasonable).

Given that I would like to continue in academia after Ph.D., I would like to hear tips and tricks on how to be a good closer.
Should there be a restructuring in the way I think about publishing?

I have recently started a PhD program but already have a good idea for a research path that could have culminated in a thesis if not for the fact that I will possibly soon be no longer registered as a PhD student. I will not be able to continue working on this direct topic, but there is a chance that I will be able to contribute to very similar research topics in an auxiliary function, so I will not entirely be “out” of the field. How possible is it, then, to use what research I’ve already done to contribute to a new attempt at a thesis at a later date?

Circumstantial information

  • The field is related to technology, so am I correct in assuming that this is likely to be riskier than in fields which are less volatile? — If my contributions are no longer “new”, I suppose they will not be able to be used towards a thesis.
  • Likewise, if I work on similar topics albeit in supporting roles, is it not possible to incorporate knowledge from these roles formally in a thesis assuming I have publications to support my contributions to said work?
  • Finally, there are administrative issues to address which I have very little experience with such as passing exams and such; Could e.g. coursework already taken be applied to the new stint as a PhD student? Is there even perhaps a “minimum time” required for working on one’s thesis, preventing them from e.g. in the most extreme case writing a thesis before even starting an academic program and then handing it in after finishing the last exam? I suppose that this is handled on a case-by-case basis, but I’d be very grateful even for anecdotal evidence.

I am a PhD student in Mathematics. My advisor proposed me a problem 6 months ago, and I did a lot of progress on it. I have reached a point where I have quite substantial results (admittedly, adapting techniques from other areas, so not a great deal of originality). They can surely form a paper, though not a great one. The original question remains unanswered in its full generality, but I felt that a complete answer would be impossible from the very beginning, and now I have very concrete reasons showing that a complete answer is indeed very hard (in the sense that a general answer/formula might simply not exist). My advisor wants me to keep working on this, but I honestly don’t know what to try anymore, and he did not suggest anything. I guess this is a common occurrence during a PhD, what should I do?

I was reading a Introduction in a Master’s thesis, and this particular example appears to be overly lengthy.

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Is there a consensus on how many points can be adequately addressed in an introduction? or a maximum number of pages? or is this question fully subjective?

The goal here is to learn more about best approaches to writing an introduction, regardless of content.

There is any manual of that what not to do in a introduction?

I was reading a Introduction of master degree and I think that in this case, the introduction stayed a little extensive.

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There is any consenso about the points that can be addressed here? or a number of max page? or this question is fully subjetive?

The goal here is improve the way to of to do a introduction , does not matter the content.

There is any manual of how not if make a introduction?

I was reading a Introduction of master degree and I think that in this case, the introduction stayed a little extensive.

enter image description here

There is any consenso about the points that can be addressed here? or a number of max page? or this question is fully subjetive?

The goal here is improve the way to of to do a introduction , does not matter the content.