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Is beautifying a dissertation looked upon unfavourably by the examiners?
I'm writing an Msc dissertation on planning space projects and I would like to add interesting bits of information on the opening page of each chapter. Though the image and the accompanying information are not directly linked to the central thesis, they add some interest around the main arguments.
Will this be looked upon unfavorably? As an academic reading this work, will this bother you or make you feel distracted / think this is unnecessary?
For example, I have this on the introduction page:

Regardless of the answer, Unis have generally a very strict format specification that you can not change for these types of documents

Ander, you are right. However, though the formatting requirements are explicit, this specific scenario has not been addressed, nor has it been stated that only (strictly) necessary images are to be included. However, to be on the safe side, I've almost convinced myself that they ought to go. They look beautiful though! sigh!

JeffE, by the examiners

How did the examiners answer this question? (You did ask them, didn't you?)

If I had the means of asking my examiners, I would have done do. My inability to do so, is the very reason, I ask on this forum. Many who have had valuable points to make, have done so.

@AnderBiguri that's very location-dependent.

@AK16 if it's got a figure number and is referred to in the text, that's probably enough justification for the picture.

@AnderBiguri why do they require a strict format, when the most important thing in a thesis is its content? A beautiful design will make a good thesis better, and a bad thesis worse. As long as the content is guaranteed, why restrict it?

@Ooker hey , I dont make the rules. I agree with you

Can you share your inability to ask the examiners?

@Ooker, The examiners are external to the university and we dont know who they are.

@AnderBiguri I don't blame you. https://academia.stackexchange.com/q/94960/14341

why do they require a strict format because they relish whatever precious little power they can exert over the few human beings they momentarily hold captive.

@AnderBiguri generally yes. Luckily, not all of them. The requirements of my ex-university is a standard front page. The rest of their guidelines consists of the sentence fonts and colors are free.

In the end this is somewhat a matter of opinion. I personally do appreciate a nice  and professionally looking layout. I am not sure however if adding images that are unrelated to the thesis itself are that helpful. If you want to start each chapter with a quote or image (which can be a nice touch), I would recommend to at least try to find someting that is related to the content of the chapter.
Most important in the end however is of course the scientific content of the thesis. If the content itself is under par, no amount of beautification is going to help you. The last thing you would want to do is to give others the impression that you spent more time on the appearance of your thesis than on the scientific content.

"The last thing you would want to do is to give others the impression that you spent more time on the appearance of your thesis than on the scientific content." +1. That is really the thing you want to avoid

Thank you for your comments, which I agree 100%. I have made good arguments, the strength of which, hopefully, does match the quality of formatting. Also, this dissertation, being a topic in the social sciences, loose connections can always be made between even disjoint ideas. In this dissertation, I present scale (size, complexity,etc.) as a deterrent to planning effectively on space projects and this image may help project the scale at which these projects operate.

Pieter and David are 100% right in practice that you need to be careful about giving the impression that you're not focussed on the science.  Nonetheless I'd like to push back a bit and say that I don't think this is a good thing about scientific culture.  It'd be more pleasant to be a scientist if everyone else put a little more effort into presenting the work better, and it might make science more appealing to more people.  And this attitude is downright pernicious when applied to teaching.  (Again I'm not criticizing Pieter and David, since they've given good advice for a young scientist.)

So, rule of thumb: Mars crater yes; Hello Kitty no.

There are also people in academia who will value that one has put the time so that the work one reviews is looking better.

For the record: I completely agree with you @NoahSnyder, it would be great if putting extra effort presenting results in a nice way would be appreciated a bit more.

The whole "make sure they know you're spending time on the science" thing is a needless concern. A graduate dissertation is not due-this-Friday high school paper. Finding neat graphics to include is just not something that takes up more than a tiny bit of your free time over the (probably) semester or longer that you're working on the paper. Plus, the content of the thesis should be the biggest indicator that they "spent time on scientific content".

In Dutch universities it is customary to have dissertations (well, doctoral ones at least) printed in nice booklets with creative graphics on the front cover which may only be tangentially related to your actual thesis - abstract art, photos of natural phenomena etc. Everyone does this.
Now, inside your dissertation is not that common, but as long as you don't go over the top it should be ok.
Specifically, in your example, you wrote:

the image and the accompanying information are not directly linked to the central thesis, they add some interest around the main arguments.

That's very vague. So, you put an image before the introduction; I'd say you should be able to actually reference that image in the introduction as an illustration of some point you're making, or of the relevant background, or of the scenario about which you've developed the thesis.
Thus if your thesis is about craters, or orbital vehicles, or exo-geology, or image processing where you use such images as inputs - then you're good; but if your thesis is about, oh, I don't know, something in topology, you might want to give this up.

More broadly, the universities in the Netherlands all used to do that (Groningen, Twente, Utrecht at the least). It seemed to me that most students embraced it as a little diversion and fun while putting their thesis together.

Thank you for your valuable advice. In this dissertation, I present scale (size, complexity,etc.) as a deterrent to planning effectively on space projects and this image may help project the scale at which these projects operate. However, in light of your suggestion, I might skip these images.

The fact that you are asking on ASE suggests that you know that this is not a common practice in your field/department. I think it's a lovely idea, but examiners have a way of being extremely judgmental about anything that deviates from standard practice.
In some sense, your thesis isn't "yours," but a product of the department. If it's something you can take to a publisher, save the design ideas for your book.

Yes, Elizabeth. I think you are right in that examiners are very particular in what they want to see. Any non-conformance, may come across as an affront to the establishment. I am taking this advice and getting rid of the non-essential embellishments immediately.

There is of course nothing stopping you from posting a second version of your thesis on your website. ☺

True..Why take the risk? I'll be left wondering whether I lost marks because of the unnecessary stuff. I can always make myself a copy with whatever images I want.

I don't know about your experiences, I've heard horror stories from arts and social sciences students. But in physics none of the examiners cared about the formal rules and rated solely content. With the extreme exception of single thesis which was horrible (the chosen font had missing characters that were replaced by fallback font and appeared huge, there were empty tables and pages, there were >10 pictures without explanation of what is displayed there etc.)

I disagree with "your thesis is a product of the department". You and only you owns the copyright to your thesis. It is yours and yours alone. That other people helped create its content is irrelevant to that. On the other hand, in order for the thesis to be accepted for a PhD degree, the department may enforce some design and or structure before they consider it. But that does not make the thesis a product of the department.

From my experience and friends/acquintances I have an impression that in arts they usually adhere quite strictly to the standards and set a required number of letters and/or pages for the thesis. In the exact sciences the situation is the opposite. The examiners either ignore the looks or value aesthetics instead of standards. And your thesis would really be valued.
There are three persons that could give you a precise answer on this, please consult them if possible:

Dean. This is the person overlooking everything in the faculty and setting the tone. This person has probably decided if adhering to the standards or
Director of programme... or something like that. For every study programme there is a person that organizes everything - plans courses, finds the examiners etc. This is a person that actually tells the examiners "please check thoroughly if they adhere to this standard". Or maybe the director ignores the standards and only tells examiners "please focus on the content unless the formatting is blatantly ugly".
Faculty secretary or whoever you hand the thesis to. This person will know in which cases the theses are handed back to students. However, they tend to exaggerate. Either because the students twist the truth ("rejected for formatting? oh boy, you really have to have everything inch by inch as written here..." when actually the font changes both the size and typeface from section to section) or they simply want to put you as far as possible from the rejectably bad formatting.

When I had to write my first thesis, the dean made a gathering and told everyone something like "please understand that the university standard is written so they could reject terribly formatted theses, take those guidelines with a grain of salt". He told us that the default LaTeX style will be better than the guidelines and we can surely ignore the Times New Roman requirement.
At the end of the day, it's unlikely that the thesis will be rejected for technical reasons. If it is, the secretary will usually be the one to reject it on the spot by saying "please re-print these pages, I can't accept it". If it goes to the examiners, an enjoyable look will most likely ... be enjoyed.
Don't know how it happens at your place, but I had a comission to whom I had to tell the contents of the thesis. Then one examiner who had to read the entire thesis, told his evaluation on the written work. The comission then decided their thing while handing each other the thesis for brief looks. I think that good pictures would be the only thing they could notice and evaluate at that point. At either point it is unlikely the decision would be skewed either ways because of pictures. Would you yourself think worse of someone for that? But you wouldn't rate a bad work higher either if it had nice pictures.
Unlike most others I suggest - surely do it! You will have a copy of your thesis for the rest of your life to enjoy. If you can get it through with the pictures, go for it! I really didn't understand my mates saving few bucks for cheaper cover, thinner paper and grayscale printing. If you like your thesis beautiful, you will not regret making it beautiful when you look back 10 years later.
P.S. I hope the picture is grayscale because of the original. Because I surely suggest you to use colour and to print the thesis in colour.

Like others I'd strongly suggest you check with the powers that be in your institute, but in the absence of a definitive answer from them, keep it minimalist.

I'm writing an Msc dissertation on planning space projects and I would like to add interesting bits of information on the opening page of each chapter.

The words "summary" and "abstract" leap to mind.
Rather than an interesting picture or text, summarize the content of the chapter in bullet points.
This will be helpful to the reader and not be distracting from your thesis.

Though the image and the accompanying information are not directly linked to the central thesis, they add some interest around the main arguments.

Material not relevant to your thesis should be avoided, IMO.  It might be seen as distracting and some reviewers may see it as a deliberate attempt to cover a lack of content (even if there is plenty of content - people get odd ideas into their heads and they're hard to dislodge).
So I'd suggest keeping it as plain and neutral as possible in terms of presentation.

Thanks Stephen, what you say makes absolute sense and I've gotten rid of unnecessary embellishments. The result looks bland but now, I don't have be anxious till I get my results.

Don't do it. Examiners are busy people, and only want to read what is crucial to the thesis topic. While they can ignore these pointless pieces of information, how are they supposed to know that? They will be forced to read everything you put in there.
You could alternatively say in the preface that these pictures do not need to be read, but that would make you look as silly as you are: "This stuff is pointless, so you don't have to bother reading it, but I am putting it in anyway, hoping to score some cheap bonus points".

As others say, it's just a matter of opinion. However, since this is a scientific work, its validity should be constituted by the content, not the design. You may want to consider whether the design will affect the judgement on the validity or not.


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Can I quote from my interviewees before the methodology section in my dissertation?
I would like to use a couple of quotes from my interviewees (primary data) before the methodology and results section. Is there any hard and fast rule about not introducing research data before the methodology used for data collection is discussed?
In summary, can I use quotes from my interviewees in the introduction section to advance a point?
edit: I have permission from the interviewees to use quotations and approval from the University ethics committee to conduct research. My question is to do with methodology.

I have been working with qualitative (semi-structured) interviews for years and I read a lot of literature that uses such data. I am neither aware of any rule that prohibits or any convention that discourages quoting interview statements before the methodology section/chapter; nor could I see any reason for such a rule to exist. It makes sense to describe the methodology before the analysis and results sections, in which you will draw on your interviews most extensively, but individual interview quotes are not equal to "analysis" or "results". They are just that: Quotes. I would also add that a catchy interview quote can be a good way to introduce a paper/book or to illustrate an empirical result or theoretical conjecture.

This is very useful. Thank you. Yes, a catchy quote uttered by one of the interview participants that emphatically describes the actual problem that my research aims to solve is what I was looking to include.

Pretty sure I've seen that before. Go for it.

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What's are some of the strangest ackowledgements you've ever seen in a masters/PhD thesis?
Typical "ackowledgements" section in a thesis thank supervisors, collegues, friends, family and/or deity.
There was a recent article on atypical acknowledgements, such as this one below:

This got me curious, Hence the question— What are some funny, witty or strange ackowledgements you've ever seen in a thesis?
Have you ever seen something hateful or negative in the ackowledgements section of a thesis?

I think this is a highly subjective and opinion-prone question with no "correct answer", but there are lots of similar questions on here that you might find interesting, e.g. https://academia.stackexchange.com/questions/95885/do-you-consider-it-as-weird-if-i-thank-the-open-source-community-in-the-acknowle/95955

The acknowledgments of my master's thesis, written around 25 years ago, contained quite a lot of "colorful" language (to say the least) and stirred a bit of commotion in the graduation board. The president wanted to remove the cum laude, but the other members eventually convinced him that I was not such a bad guy, after all. The secretary of the student office who received the copy of the thesis (at the time on paper) later said: "I knew he was up to something, he was grinning while handing in the copy!".

Can you please give the reference (or a link) to the paper that discusses unusual acknowledgements.

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Group Project Dynamics— how to present advanced ideas without offending others?
For my research masters, one graded activity is to undertake a piece of research and present findings.
We collected data and the analysis part was self-assigned to one member and myself. The analysis and results ultimately presented by the team member was very basic, and included nothing more than a sum, count and percentage of responses presented in an excel graph.
The supervisor responded that he was unable to follow findings and suggested advanced analysis.
I then undertook this work, redid the analysis in R, and presented the findings in an advanced/visual form, using regression analysis and correlation matrices and suggested the group explore data/findings and suggest additional hypotheses to test.
The member who originally did the analysis feels let down and asked meaningless, trivial and baseless questions when I presented findings, in an effort to discredit my work. She felt I was trying to better her.
The supervisor is yet to comment on the validity/usefulness of my analysis work.
In my earlier degree, I gained a good understanding of research methods and statistics. However, this person, from what I’ve seen, does not seem familiar with fundamental research concepts or statistical methods.
How do I handle this situation?
How important is it to regress to ideas that everyone is comfortable with to avoid challenges like these?

Why not simply take a little time to explain to her what she seems to be having difficulties with?

@David, I’m afraid that doing so might come across as patronising. As such, the team member is defensive when I explain a concept. She resorts to googling bog standard and well known drawbacks of anything I explain and using that to raise concern.

Someone commented (the comment has now disappeared) that it’s best I let go. I think this is what I should and will do. I wonder how many groups lose out on good scores due to team members unwilling to learn or admit so do e may know more? Ah..well.

This seems to be more about interpersonal relations than academia.

The group research exercise is academic and consequently will affect my grades.

You don't have to do anything with this situation any more - your colleague is clearly feeling uncomfortable about it and as you said in comments, reaching out to her now might very easily come across as patronising.
For the next time, I'd advise going through the analysis with her before presenting to the group. Ideally, you'd do this a couple of times as your analysis progresses, and take her initial results as a starting point.
In analyses and presentations, you should never shy away from using complex methods if you feel they are necessary. However, you should also be able to explain them in such a way that your whole audience can understand what you are doing and why.


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Changing thesis supervisor to avoid bad letter of recommendation from current supervisor?
I entered current school as a Master-Ph.D. integrated course in high energy physics, however, decided to graduate as a master in this year and will apply abroad in the next year since my research interest is different from that of current supervisor. The defense is upcoming, indeed is on this Friday.
I'm considering changing MS thesis advisor because I'm uncertain but I guess my current supervisor may write a bad letter of recommendation.
I had several troubles with him mainly because his research field and style is different from mine. He implicitly mentioned that I took other student's chance of being his student because my interest was different from the beginning. I asked him explicitly if he can write a letter for me, and although he said he will write the paper, many of what he said after this implied bad contents. For example, I asked him if I regularly report what I'm doing in next year, whether he can re-evaluate me or not. He politely rejected this saying once I graduate from his lab, I and his life should be independent and he shall not care about mine. He also mentioned that evaluation should arise naturally, not in such an artificial way.
My defense is on December first, so it's within this week. But I have been seriously considering to changing supervisor and to postpone graduation to the next semester. I already got approval from a faculty to whom I want to transfer. He also explained the project I will take on if I change the supervisor to him.
However, I am very worried that it would be very rude and inappropriate behavior to change supervisor due to the worries about a bad letter and within such a short interval before the defense. I'm also worried that doing this will make current advisor as an enemy. But I am confused because he had suggested me to change the supervisor at the end of March this year.
If I graduate this semester and get a bad recommendation letter next year, even though I got strong ones from other faculties and researchers, will it be a significant weakness in the Ph.D. application?
Can any advice or opinion?
Thanks.
P.S. In a comment I left, I mentioned that my advisor rejected advising me. To be specific, he told me that he is very busy so he will not advise me in person but instead he will ask two PDs in the lab for my thesis. He seemed very busy, but I was first student of his the only two students, and I know that he is regularly advising and being in discussion with an undergraduate student for his graduation thesis.
Also, he explicitly mentioned that since he doesn't trust my ability, he can't allow extending my work beyond the elementary scope when I asked him for adding contents beyond the Standard Model on my thesis.
P.S.2. I am quite certain that I can get a good reference from the faculty to whom I want to transfer. Even though I graduate this semester, I'm planning to work with him next year. In this case, if I have a bad reference from the past supervisor while also have a strong one from another faculty with whom I will work next year, will it seriously affect my application?

" For example, I asked him if I regularly report what I'm doing in next year, whether he can re-evaluate me or not. He politely rejected this saying once I graduate from his lab, I and his life should be independent and he shall not care about mine. " To me his reply seems perfectly natural, especially if you're not really interested in the same things?

@starless I agree with you, however, my advisor repeatedly told me that ‘I haven’t shown him any outcomes.’ That’s why I need re-evaluation after working with others in next year.

@dan1111 "It seems like an outcome to me..." during the project with him and another PD, he showed me for many times how he was disappointed that I didn't make any outcome. He also refused to advise me on my thesis work while he is doing so to an undergraduate student for his graduation thesis. From this, I guess he doesn't have a good impression and expect on my research ability and potential. May these be hint of bad reference?

@dan1111 "How is your advisor refusing to advise you?" To be specific, he told me that he is very busy so he will not advise me in person but instead, he will ask two PDs in the lab for my thesis. He seemed very busy, but I am a first student of his the only two students, and I know that he is regularly advising and being in discussion with an undergraduate student for his graduation. Also, he explicitly mentioned that since he doesn't trust my ability, he can't allow extending my work beyond the elementary scope when I asked him for adding contents beyond the Standard Model on my thesis.

It sounds as though your advisor is an advisor only on paper as he declined to work closely with you after you decided not to work on a topic that was relevant to his research interests ("his research field and style is different from mine. He implicitly mentioned that I took other student's chance of being his student because my interest was different from the beginning").
It is normal for Masters students (and in many fields, PhD students, too) to work on topics proposed by their advisors, and while an assertive student with strong ideas of their own might persuade an advisor to support them working on an unrelated topic, it is understandable that your advisor might have been disappointed if this meant that he missed out on getting a student to help further his own research. So -- that's in the past and there is nothing you can do about it now, but I am guessing that is what has happened.
I would recommend that you not use a recommendation from this advisor, but I don't think that means you necessarily need to spend another semeseter working with another advisor. Although in the ideal case, you would have a strong letter from your Masters advisor, you can also use strong letters from other academics that you have worked with, and not ask your Masters advisor for a reference letter at all.
If you have no money concerns, no deadline for graduating, and no need to get on with the next steps in your life, it certainly might help in a PhD application to have stayed an extra semester and achieved stronger research outcomes with a more sympatico advisor... But an extra semester is still an extra semester of your life. Depending on where you plan to apply, it might mean delaying your PhD application by a full year.
Things to ask yourself:

How strong would your PhD application be without that extra semester? If your grades are outstanding and your research project was good enough, you probably don't need it.
How strong would your PhD application be with the extra semester? If your grades are merely "good" and your new research project is anything less than outstanding, it might not help.
How essential is a PhD to your plans for the future? If the PhD is an absolutely essential next step, the risk-reward ratio might be different.

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how can i make peer review before publication?
How can i get peer review of my research before submission to Journal of Dental Research
Osseous Reaction to Three Different Capping Materials : Experimental Study

That you need to ask this question in this forum might mean something is really wrong..

Two options:
You show it to your peers or colleagues and get their feedback, or
You submit to a journal who will send it out to review if they don't reject it "of the desk".


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How can I gain access to a database to which my university does not have access
I'm a PhD-student in economics and I'm planning a scientific paper on a certain topic. For this purpose I need access to data from an economic database. There exist several companies, which provide the desired datasets in their databases.
But the problem is that my university has no access to any of these databases. It is also possible to buy certain datasets there individually, but my research budget is far from sufficient to buy this data.
So I would like to ask if there is a possibility to get such data on another way?
I had already considered whether it makes sense to write this article with a researcher from another university that has access to one of these databases.
The disadvantage in this case is that the name of the other researcher stands on my paper without having made any significant contribution.
Furthermore I thought about doing a short research stay at a foreign university, which has access to a database. However, this is hardly feasible under the current global circumstances.
Unfortunately, my PhD-supervisor also has no answer to my problem.
I would be very happy if someone in a similar situation has found a solution and can tell me about it. I would like to ask not only specifically in the field of economics but in general. It may be that the solution can be transferred from another area to mine.

Have you considered just asking them for access to support your research? It is a long-shot, of course. Even asking for a discount that is within your research budget. It might help if your advisor has sufficient credibility.

I don't think this comes into consideration, since I am their customer and want to buy their data

It might depend on the data and on your research. Having it done might actually benefit them, depending. You may not be a typical "customer". But the "libraries" answer of Raghu Parthasarathy might work if you haven't already tried that.

I would ask your university librarians -- they tend to be resourceful and under-used! At least for journals to which the university does not subscribe, there are typically ways to request single articles, often through inter-library consortia. I wouldn't be surprised if similar things exist for brief access to databases.

This is why your university has librarians.  If your institution is large enough, it probably has a specialist digital librarian.

That's a good idea. I have not yet heard of such an "exchange" for databases, but I will ask.

And your librarian may be able to advise on guest membership of another university's library, that then gives you access to their database subscription. Or your librarian may be able to suggest access to another dataset that is usable for your purposes (such as a different industry, different country, or a smaller but freely-available dataset). Or you might be able to rent access to the data for a short period enabling you to do your research, but not buying permanent access to it.


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How important are the grades in some of the master courses for the PhD admission (with forward view on France, US, UK)?
I was enrolled in MS (computer science) studies some 7 years ago and I acquired 40 credits with rather low grades (5, 6, 7, 8, medium could be 6.7). This MS program was a bit different from my BS studies and also I had to work in almost full time job and there were other problems. I completed BS (physics) studies with the average grade of 9.7 and I had and I have quite a bit ambitions. So - these 1.5 years of my life was some bad period. It can be explained partially with my ambitions to master quantum field theory (initially) and then theoretical  computer science and AI (later).
Now I am restarting my MS studies from the scratch. I learned all these years myself, in my own pace (textbooks, online lectures, articles, PhD thesis - both computer science and math - there are really cool connections for building deep learning theory), mostly I tried to achieve the level when I could freely read and do the advanced staff, like HoTT and theory of deep learning (beyond the empirics that is so cool in the industry). And while I still have to work, I am far more confident about my job. So - I feel that my MS studies will be far better this time.
Now the question is about 16 (from 40) credits which I am eligible to transfer from my previous studies. I am doing utmost to have the highest grades now, but I should decide whether I want those 16 credits with 5-8 grades on my diploma, or maybe it is better to go through these courses again (it will be easy) and get better grades?
I have 2 arguments in my mind:

if I am transferring grades from my previous period, then I have prospect to shorten my studies from 4 semesters to 3 semesters. There is no monetary reason, but I would like to be PhD studies eligible sooner.
my goal is to enroll in some PhD (computer science, AI, deep learning theory, connections with math) studies in France, US or UK. When I told that I learned myself, it meant, that I really took the advanced staff. So, I am setting my bar very high - Grande Ecoles or the best in US, UK. I am doing programming, math and I am writing some stuff for possible articles, so - I hope that will have more or less some papers by the time I will apply for PhD studies. But the question is - how important will be the grades for those 16 credits during the application process?

I am pretty sure, those 5-8 look very bad, but from the other side I hope that I can explain the reason for this and I hope and plan that I will put the best grades in the remaining courses plus some very good research articles in the other plate of weights? Can it work?
Resp., the question is - can the best grades in the remaining courses (44 as opposite to 16), the best master thesis (20 credits) and the important research articles outweigh the bad grades for 16 credits in my diploma wrt the enrollment prospects in PhD studies in France, US, UK?
I am pretty sure that the competition for BS and MS studies are pretty fierce and formal. But the enrollment in PhD studies can be more intricate matter. It is pretty formal in my present country (Eastern Europe, here I can not hope to pursue PhD studies with such grades on my diploma, I can only have self-financed studies), but I guess - it can be pretty different in France, US, UK? Maybe they have no formal formulas for grades+thesis+papers and maybe they evaluates the prospective PhD studies how well they can do research, what advanced skills they have, how hard working they can be in acquiring the frontier material, do they have incentives and are they willing to transfer the research output to the industry and maybe create their own startup? And these things are only partially reflected in the grades that were acquired some 7 years ago.
And the last thing. Maybe money can solve this problem? Maybe I can plan to have such a good job (or startup) that I can afford to pay for my PhD studies and then the admission criteria can be relaxed wrt those 16 credits?


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Criteria for acceptable integrative research
Conventional research almost always involves the research of only one aspect, problem. But sometimes, I feel, there is need for integrative, holistic, synergistic research projects. Let's consider artificial intelligence and neural networks. Almost always the individual research is around the specific problems - e.g. about implementation of one specific logic in neural networks, implementation of specific language model in neural networks, implementation of visual recognition in neural networks and so on. But the practice invites use to consider the whole system, cognitive architecture, e.g. that can use the synergy of common language/visual/auditory/tactile semantics to complete multiple tasks. Such synergy can lead to improved results and hence - it is welcome from the Science as well. There is indeed the research about cognitive architectures but it is far, far less in amount and gaining far less support than the research of specific themes.
My question is - what considerations should be taken into account when one chooses the integrative projects as the theme for his/her master or PhD thesis. What can go bad? I have heard about specific requirements, advice for the doing the systems research, maybe more warning and more suggestions can be made about the acceptable and good integrative research.
I guess, such integrative research of end-to-end systems is actual not only in the cognitive architectures, but also in systems biology where one is eager to consider multi-scale models of full organisms of even full ecosystems. Maybe such research should be done in late career only? But what about the cases when internal drive, interests and perception of topicality make one to choose such theme for master of PhD thesis and nothing less is enough for keeping the momentum of drive and flow.
p.s. with the advent of the category theory there seems to be the right toolbox for such integrative research and so, maybe today, where so much facts and ideas are gathered, maybe today is the right time for doing good integrative research.

Lets or let's ?

"Acceptable" is a pretty broad term. Acceptable to who and for what? Actually, any ethical research is acceptable in a general sense.
However, since you seem to be studying for a MS, I suggest that "acceptable" primarily means "acceptable to your advisor" if that person must judge your work at the end. Take their advice and ignore others as long as you are a student - even a doctoral student. There are very few students who can leave their advisor behind, though they exist.
You make a good point in your question, however, by asking if this sort of research is best left to late in a career. There is some wisdom in studying very hard problems later, after you already have both a reputation and cohort of collaborators with whom you can share ideas and develop synergy. This is true in any kind of high value - high risk research. If you wind up with nothing doing problems that aren't "ripe" for solution, then you never get a chance to develop.
However, that doesn't mean that you have to ignore hard problems. Wise researchers keep a "problem notebook" in which they write down ideas they have for questions worth answering along with thoughts toward solution. If you have an idea about one of your listed problems, jot it down in the notebook. Carry index cards with you so that you can write down quick ideas even if your main notebook isn't with you at the time. Then transfer the notes when you get a chance. When you are bored, scan through a few of the problems to see if you can now make a bit of progress on them.
But spend most of your time as a student and in your early career working on things that can be accomplished without too much risk. And you don't have to work on things in strict sequence. Do what you must to solidify your career. Do what you can on the big questions.

You need to make sure your project can be finished within the time limit available. The main problem beginning researchers seem to have is delimit their project such that it is possible for them to get it done.
The other challenge will be to find a structure for your text that would make it readable. If everything depends on everything, then where do you start?


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What is the personal economic motivation and benefits (in the international community) to publish in good but non-SCI or non-SCI-Expanded journals?
While SCI/SCIE journals (journals included in the Clarivate indices of Science Citation Index, Science Citation Index Expanded) are the gold list of journals and (my guess) the truly international science community considers only the publications that are published in the SCI/SCIE journals, there are some good or emerging journals which are not in SCI/SCIE. I know that some countries (e.g. Easter European countries) in their idiosyncratic evaluation systems allows the SCOPUS and other publications to count as real publications, as I understand, then the serious and international community considers only SCI/SCIE publications as serious ones which can be counted for the evaluation of research proposals, grant applications and position applications.
But as I said, then there are good journals outside SCI/SCIE as well. E.g. I am trying to do research in the nascent field of Artificial General Intelligence (e.g. there is Third Wave AI Campaign by US DARPA https://www.darpa.mil/work-with-us/ai-next-campaign) and Computational Creativity and I am looking on two journals in their respective fields: Journal of AGI https://content.sciendo.com/view/journals/jagi/jagi-overview.xml and Journal of Computational Creativity https://jcc.computationalcreativity.net/. The first journal has rather long history and it has very high standard as I can judge from the published articles (this is my subjective evaluation, but I believe my gut feelings to distinguish bad research from the good one). But the other journal is still awaiting its first issue.
My senses (trying to be good person) advises me to prepare papers exactly for those 2 journals to support the community (of AGI and CC that is still growing and still awaiting status deserving to them), to support the status of the journal. But as those journals are not included in SCI/SCIE, then rationally thinking it would be waste of energy. Instead I can tweak my papers and submit them to the traditional journals (e.g. Cognitive Systems Research or Physics of Life Reviews by Elsevier).
But it is not an easy decision. When I am seeing that from time to time Journal of AGI still receives very high quality articles, I need to ask - why researcher still submitted to this journal knowing that his or her publication is not counted in the indices?
So - what is the personal economic (mercantile) motivation and benefits to publish in high-standard non-SCI/SCIE journals? I am interested only in the mercantile motivation as I clearly understand the social/emotional/moral motivation.
And just curios - why Journal of AGI is not listed in SCIE? Because it is small-scale journal (articles are published rarely)?

As a member of the "international science community," I've never heard heard of SCI/SCIE, so I suspect this is field-dependent. Sounds like CS.

SCI/SCIE - Science Citation Index/Science Citation Index Expanded. If you are coming from humanities and social science then it may be possible that this is no concern of yours. But I guess in humanities and social sciences there are such Clarivate or other indices as well.

Do you mean "economic value"? Mercantile has to do with selling. And do you mean the value to the authors or generally, say to society.

I explained - personal value coming from publication counted/evaluated as good publication when I submit grant application (to public or private funding agencies like European Research Council or Simons Foundation), application for University position. As I understand then evaluation of applications and CV are based on SCI/SCIE publications and not on other publications, at least in developed countries and high-ranking Universities.

I guess that it is good to strive to be acceptable by the funding, University standards, it is nothing to be ashamed for. I can have secure funding, then I can do research and in such manner I can contribute to the society. Play by the rules and give back even more. That is nothing bad, although there are many, many ethical discussions. My research can advance the automation and hence end of human exploitation and so - I should do everything possible to secure my research.

Btw, I would be happy to change the title of my question if I knew better word that can be used instead of "mercantile". I guess, this word creates misunderstanding. As after posting my question I am again banned from Academia site (I guess that there are closevotes already happening). I am just trying to be good person, but this banning is so hard. Banning without real explanation or consideration. Something like Cancel Culture.

"the serious and international community considers only SCI/SCIE publications as serious ones" I'm fairly certain that you are simply mistaken about this.

@Maeher This depends on your field & international context. In scientific fields in China for example, only SCI/SCIE publications are considered in advancement and evaluation decisions. Representative pubs for grant applications similarly must be SCI/SCIE-indexed.

To give a concrete example: a lot of papers in medical imaging and other fields of CS are published in LNCS. But LNCS is not listed by Clarivate, because it is not a journal.

Clarivate has another index https://clarivate.com/webofsciencegroup/solutions/webofscience-cpci/ and I guess - it is quite prestigious as SCI/SCIE. Btw, maybe you can open Academica question about the prestige, use and parallelism of citation index for proceedings and relation to SCI/SCIE. I am currently banned from asking new questions in Academia - apparently peers didn't like this question.

@TomR I am not. Please edit your question to be specific to the field you intend it to be.

You are massively mistaken in your assumptions.
Generally, the experts know the rough reputation and standards of the publication venues in their area. If they are not familiar with a specific one, then looking at the recent papers and the editorial board, or asking colleagues will typically be preferred to a binary check of whether or not that venue is listed on a particular index. There certainly is a strong correlation between "is a reasonable journal" and "is listed in SCI/SCIE", but it is the former that matters, not the latter, when being evaluated by experts.
For grant applications, none of EPSRC (UK), Royal Society (UK), ERC, DFG (Germany), NWO (Netherlands) restrict applicants to only list publications in SCI-listed venues. As someone who was on the job market a few years ago, I've never seen any job advert asking for only SCI-listed publications either.
The UK has the Research Excellence Framework (REF), a huge exercise to judge research quality of all researchers at British Universities. Everyone submits their top 5 published papers, and there is again no restriction to SCI-listed venues.
I've been around in the CS/Math area for 12 years now, and in fact only directly encountered a situation where something being or not being listed on SCI(E) was relevant: We were planning a special issue dedicated to a conference, and as one of the prospective author was a Slovenian PhD student who needed SCI-listed publications to graduate, we picked a particular journal. For colleagues from South Africa and Russia, some journal indices seem to matter, too.
So unless you consider it likely that your academic career will substantially take place in a few select countries, it really won't matter whether the journals you publish in are SCI(E)-listed or not.

I dont understand this. Your journal should be in SCIE to be included in JCR (journal citation report) and have IF (impact factor). How it is possible to have internationally recognizable publication without IF and not being in citation report. In computer sciences even the Lecture Notes in Computer Science proceedings series have IF for at least some of volumes. E.g. proceedings of AGI international conference is published in indexed volume of LNCS. So, AGI is not alchemy indeed.

@TomR A journal is internationally recognize, if the relevant experts recognize it as a reasonable journal. Nothing more, nothing less. Those indices matter way less than you think they do.

Possible reasons are:

Invited by personal contacts (typically members of the editorial board of the journal).
They believe the new journal will rise up the ranks quickly. This happened for, e.g., Nature Astronomy (which is in turn likely to rise up the ranks quickly because the journal is backed by Nature).
Ideological reasons. Example, example.
They don't think their paper is particularly good (or it has already been rejected by top journals) so they figure, why not.

That's about it.
With all due respect to the other answers, statements such as "If the journal as a whole is widely read/cited by your academic niche community ... " are misleading, because if the journal is widely read/cited by your academic niche community, it won't be very long before the journal ends up in the SCI or SCIE.

I disagree with others who say you are mistaken in your assumptions. There are many countries where SCI/SCIE indexed publications are the only publications considered in academic hiring, grant review, and advancement evaluations. Even in countries like the UK where this may not be an "explicit" standard, the majority (though not all) "highly respected" journals are already SCI/SCIE-indexed. Academic chauvinism exists in both explicit and implicit forms. However, I can think of a few reasons why it might personally benefit your grant or employment prospects to submit to a non-indexed journal:
(1) You believe/know that the journal will be indexed in a few years. This might happen if the journal is new, but is part of a suite of society journals where all of the other journals are indexed (i.e. if the American Chemical Society launched a new journal, it will definitely be indexed in a few years).
(2) If the journal as a whole is widely read/cited by your academic niche community, then even if your article is not explicitly taken into account by your university or grant agency, it can still increase your stature within your field, getting you invited talks, etc., or boosting the citation rates of your other SCI-indexed papers, and increasing your chances of receiving funding, positions, etc. through these "knock-on" effects.
(3) There would be social/relationship benefits. For example, there is a "big shot" in your field who's taken an interest in your work, perhaps they run their own journal that for various reasons might not be SCI-indexed (for example, it's a journal in the local language, like a Japanese-language journal). Thus, it might be important in the home-country's scientific community, but have little international reach. If that big shot invites you to submit something, then contributing an article to their journal is a great way to build your personal network. Later on, they might help you get shortlisted for a position at their university, give you an invited talk at a conference they're organizing, etc.

Using indices in hiring and grant review is not sensible.

@AnonymousPhysicist I fully agree it's not sensible, but that doesn't change the fact that it is common practice!

No it isn't.  It may be common in certain countries, but it is not common across academia.

doesn't "common in certain countries" mean common across academia if you are part of that country's academic system?

No.  Academia is a global endeavour.


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Is university allowed to refuse to confirm my masters theme due to lack of appropriate research staff?
I am paying for my studies and I have chosen my own theme for my MS thesis. It is in artificial general intelligence and symbolic methods, rather rare theme. I am promising to do all the work fully myself, with some guidance by external researchers. Noone in the department is willing to be the advisor for my theme. I consider this research direction to be of utmost importance, in any case it is far more superior than the machine translation themes to some minor language - themes that are proposed by the department. I am not asking the would-be advisor to be expert in the field, I am asking just formal signature on the papers to continue my own research. Can university refuse to give me advisor? Maybe I can ask my money back? Maybe I ask the university to improve its quality and topicality of its research directions?

Can you edit the title? It isn't true that the university is refusing to confirm, they're unwilling to authorise a certain topic, because it is out of scope.

I will just try to be nice. I hope that every researcher is open for prospective challenges, ideas, efforts, for gaining new knowledge and insight (even more if those help humanity), that is why they are at the University :).

@TomR Are you implying that the faculty in your department is not open? They probably are open, but they also have other responsibilities and they thus need to prioritize. A good case can be made for interdisciplinary research, but an equally good case can be made for specialization. I know you are upset that your priorities don't match their priorities, but such is life. This won't be the last time you will have that experience. A productive strategy to deal with those instances won't consist of blaming the others for not sharing your priorities...

The way you ask the question makes it seem you really don't understand much about research, which would lead me to guess that you really shouldn't be attempting the stated topic. How about you listen to the reasons your professors are giving you for why your topic is not being approved--your professors are almost surely right.

Do you already have the external researchers agreement to help? If you don't, that could also be a sticking point.

See this from the point of the department: They are giving out diplomas saying "we have seen the work produced by this student and certify that it meets these requirements". If there are no specialists in a given topic, how can they certify that you meet those requirements? When you choose a department for doing your masters then it is up to you to determine that they are doing the things you are interested in. So yes they can and should refuse if they feel they don't have anyone with the expertise necessary to evaluate your work. You can always ask your money back, but in all likelihood they refuse.
You can always ask the university to cover different topics, but universities cannot cover all topics so choices have to made. Adding a topic typically means hiring one or more additional professors. That is a really big long term investment. They are not going to make that investment because a student (who is almost done anyhow) wants that topic.

Surely the department can evaluate a dissertation even without a specialist?

@user2768 Just think about the related work section. Without anyone familiar with the research area, how will they be able to tell whether the dissertation omits related work that should have been cited and described?

@PatriciaShanahan Researchers rarely no if related work sections are complete, this is typical of peer-review. Whether such knowledge is necessary depends on the grading criteria, which will partly depend on the type of dissertation, e.g., research-only vs. some other deliverable.

@user2768 It depends on how far removed the topic is from the expertise present in the department. Often disciplines are split up in a large number of sub-sub-sub-sub-disciplines. Some can talk to each other, and between others there is basically no common ground.

@MaartenBuis I don't disagree, but I'm not sure that's particularly relevant, since taught courses may be removed...from the expertise present in the department. This isn't surprising, because teaching material from a textbook isn't particularly difficult.

@user2768 The OP is not looking for someone to give her or him a course, but is looking for someone to sign off on her/his Master's thesis. A master's thesis is supposed to be quite a bit more than a regurgitated textbook.

@MaartenBuis I know; the teaching example was only intended to illustrate that academics are capable of operating outside their immediate research area.

No body should be giving lectures from a text book at a master's level. I definately would demand my money back if the stuff I was being taught on a masters was out of date enough to be in a text book.

@user2768 IanSudbery is right, and a thesis should be even more current.
Your thesis might take for granted things that readers won't know (tho it shouldn't), and only experts might know what subtle mistakes to watch for.
A master thesis gets close to the frontiers of knowledge, even tho it isn't quite a PhD. To understand "frontiers of knowledge" properly, see http://matt.might.net/articles/phd-school-in-pictures/.

@IanSudbery Do you have evidence to suggest that no body [is] giving lectures from a text book at a master's level? I strongly suspect many are and that standards vary significantly country-to-country. Regardless, my point remains: A master's level course can be taught by a non-expert, in particular, an academic in the broader field, e.g., a computer scientist could teach artificial intelligence.

@Blaisorblade Non-experts examine PhDs, so surely non-experts can examine master's level work?

@user2768 No, experts examine PhDs, and non-experts (even sitting on a committee) will rely on experts; for a PhD, “expert” is defined even more restrictively than for a master thesis.
EDIT: re master courses, standards vary; yet, one taught by a non-expert will be significantly worse. My PhD advisor once mentioned this explicitly.

@Ian Sudbery: No body should be giving lectures from a text book at a master's level. --- This is apparently extremely field dependent, as it almost wouldn't occur to me that something at the masters level would be so specialized and new that it wouldn't be in a textbook. Indeed, Springer's Graduate Texts in Mathematics (280 titles) is one of the best known book series in all of mathematics, and there's also the AMS textbook series Graduate Studies in Mathematics (206 titles).

@DaveLRenfro Fair enough — tho would you have a non-expert teach that? (Say, an algebraist teaching analysis, or viceversa?) But I can believe maths has more standard curricula.
OTOH, topics CS for “artificial intelligence” are likely less stable. My field (PL) has some textbooks, but you’d still want a specialist to teach about it.
Finally, even if an expert of A can teach B, it doesn’t follow they can supervise topic X picked by a student. Or in fact, that they accept X as being worthy of study (EDIT: at least by them, and possibly by others).

I don't understand what the discussion is about: There are degrees to being a non-expert. I am from sociology, and there are many sub-fields in sociology that I can understand even though they are not my thing. There are also many other sub-fields that, even though they are supposedly writing in a language I understand, I don't understand a word they are writing and I don't know what they are trying to achieve. I will happily grade a thesis from a former sub-field but not from a latter sub-field. I cannot see why that is controversial.

@user2768 - I have no evidence because thats not what I said. I specially said should not is. In my dept we don't teach from textbooks beyond first year undergrad. I reckon given enough time, I could teach pretty much everything at first year, and probably about 50% of the second year, but I couldn't teach even a fraction of the third year courses, without effectively doing another degree myself.

Approach an advisor that you already have a relationship with. Explain that you are an excellent student, providing grades as evidence, if that's true. (If it isn't, I wonder whether pursuing this topic can be recommended.) Explain why you are passionate for artificial general intelligence and symbolic methods,  why the research [is] of utmost importance, and perhaps mention why it is superior [to] the machine translation themes...proposed by the department (being careful not to criticise the potential advisor's research domain). Explain that you have found external researchers willing to guide you, providing letters of support as evidence, if those researchers will provide them. You must deliver your message succinctly and coherently.

Can university refuse to give me advisor?

The university aren't refusing: You are refusing to find a topic that the university is willing to supervise.

Maybe I can ask my money back?

Sure, but you're seemingly at fault, not the university, and I think it is extremely unlikely that you have any chance of getting your money back (at least, not in the context of your question).

Maybe I ask the university to improve its quality and topicality of its research directions?

Sure, but it won't help you. (Change takes time.)

Surely you should have agreed the “theme” with your advisor for it to be accepted and ratified by the institution.
If you have not, then you might need to choose a different topic.

The OP doesn't have an advisor, they cannot find one willing to supervise their "theme."

@user2768 some or many places organise the advisor first, then sort the theme...

Thanks for the clarification. That seemingly aligns with my comments (in response to Maarten Buis's answer): Academics can examine work that is outside their expertise. (If that weren't the case, then assigning advisors first wouldn't work, unless advisors have veto rights.) Others disagree with my comments...

@user2768 No, I did not say that... Many places use the skills or areas of their faculty to control the range of themes possible.

How does organis[ing] the advisor first then sort[ing] the theme work, if advisors won't supervise certain themes?

@user2768 because the student discusses the theme with the advisor and they reach an agreement. This seems most logical and was how it happens in the several places I have worked...

I think I get it: There's some pre-selection, whereby you organise the advisor first, based upon their themes, then you negotiate more precisely later


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What to do during Math PhD to have options in industry?
I have recently been accepted to a top five math PhD program. The department seems to have a good mix of pure and applied math, and there are plenty of opportunities for collaboration with other departments (Stats, CS, Econ, etc.)
I am most interested in pursuing math more on the applied side of things. Moreover, I would like options outside of academia once I graduate. Looking at the previous PhD alumni, this does not seem to be a problem - anyone not in academics is doing pretty interesting stuff in finance, data science, consulting, machine learning. However, I want to make sure I am not missing something along the way that these students did which enabled them to have broad and attractive job prospects.
I am especially interested in Analysis, Probability, Stats, Machine Learning, Econ, Mathematical Biology, Cryptography, and Applied topology. I would be happy to do work involving any one of these.
My questions are these:

What should I do during my PhD to be able to have good non-academic job prospects after graduating? (Would something like a PhD minor be helpful?)
How does it differ by field? If I want to do something like quantitative finance, what do I need to do versus if I want to do something like data science?
How can I find out more information about question two. As things change and evolve, how can I find out what it is I need in order to be accepted for positions and jobs involving areas that interest me?

I am not sure if I am exactly asking the right question, so if someone else has suggestions of what I should be asking, please suggest.
Note: There are a number of questions on this site that ask similar questions, ("I'm doing math but I want to go into industry") However I think my question is rather different. Firstly, I am hoping to pursue research with applications during my PhD, rather than focusing on super pure math. Secondly, I am asking this question before I have even started my PhD, hoping to know what I should do before, during, and after my program. Most of the other questions basically have the theme "I did a PhD in pure math, now what?"

Your question seems pretty broad, especially since you haven't even narrowed down a field. It's also a bit borderline for being on-topic to me, because it's not really about academia but about job prospects outside academia. In general though, if you do a PhD in an applied math area where applied means applied to something done in industry, you are unlikely to have any issues with job prospects based on what you've done or haven't done during your PhD.

I think this is a very relevant, and legitimately scientific question, even if not so good for this site. The fact that the majority of math PhD's currently seem to be getting jobs outside of academia has only recently been widely acknowledged, and there seems to be very little public discussion about what this might mean for graduate programs' content, etc. Maybe this question is too math-specific, but I don't think it is "too broad". Difficult, yes.

Not a real answer, but a place-holder: until some years ago (I do not have precise details), perhaps a majority of math people did go into academe, at least initially. As quite a few of my own students from years ago found, in those days a PhD in math from a good school was a ticket, in itself, regardless of specific PhD topic. The point was that it was pretty difficult to do, and required being able to do basic math reeeeeally well. Truly great general analytical qualifications for any sort of job. But... nowadays, yes, you are competing with lots of other people with... [cont'd]

The problem is the objective itself is extremely broad. How will any one person here know whether it is better to supplement your phd by learning to program versus learning to predict sticks versus learning french? People can only suggest directions that are practically orthogonal.

[cont'd] ... comparable qualifications. Having a PhD in math from a good school is not longer a golden ticket out in the real world, though it's still (as far as I can tell) better than lots of other pedigrees. So, yes, plausibly, targeted study could be good. However, established curriculum is inertia-laden, in violent contrast to of-the-moment practices in banking, etc. The curriculum will surely not really keep up... Obsolete viewpoints are almost worse than ignorance... ?!?! I don't know.

@ASimpleAlgorithm, a former student of mine launched a solid career by being fluent in Mandarin. We can see where that goes. It's actually not mysterious... though is not abstractly-understandable. That is, unlike the long-term-stable ideas of mathematics, the question of "what is immediately useful?" is volatile. Not inexplicable, but not abstractly deducible, and seemingly-randomly changeable.

Line up summer internships, they'll give you a taste of what's out there.

Based on my own interactions with graduate students and post-docs, I would suggest:

Develop skills that are useful to potential employers.  This includes basic communication skills and a broad background in data analysis and modeling as well as buzz-word computer skills (R, Python, Deep Learning, etc.)
Develop cultural-competence for the corporate world.  Learn how to dress and act the part, preferably by immersing yourself in the corporate world through an internship.
Have some examples of your work (besides your papers and dissertation) to show to potential employers.

Another important factor here is choice of advisor. Look through the faculty profiles and see if you can find a good fit. This would be someone with industry experience, or that has managed to assign their students to industry jobs past graduation. It would be good to be advised by someone who'll be able to help you get internships at relevant companies and direct your research in the direction you want.
In my experience, math PhDs (with a smattering of CS/Econ/Stats) are highly employable in industry, even if they err on the purer side of research.
I know several people who studied pure math and applied their problem solving skills in internships with very good outcomes, let alone if you are learning applied skills on the way. Other soft skills that you acquire will be extremely useful in industry - writing papers, speaking skills, problem solving and so on.
PhD studies are also an excellent time to try out transitioning your ideas into startups. Most good universities have tech connections to help you do that, try to look into that.
Good luck!

As a former arithmetic geometer currently on the industry job market, here's some advice/observations, current as of 2021 (things in industry do rapidly change though, so stay vigilant). I'm speaking from a US perspective and don't know anything about how this changes in other countries. I'm also talking about the labor market in a big US city with a large tech sector (e.g. SF, Seattle, Boston, NYC, LA...) - regardless of where you get your PhD, you'll likely find way more industry opportunities for math PhDs somewhere like this.
General advice:

networking is really valuable, both for general knowledge about industry opportunities and for finding actual positions. Keep up undergrad connections, try to meet people who do quantitative research outside the math department (stats, econ, CS...) e.g. by going to their seminars, and don't be afraid to ask your friends to introduce you to people in their network who might be willing to help. Your university's alumni network is also a good place to look.
Industry is way more flexible than academia and evolves rapidly; as a corollary, it's much easier to pivot your interests than it is to e.g. change research specialties. Math is awesome because it's extremely portable and ubiquitous, so definitely don't talk yourself out of opportunities because you have the "wrong" experience.
Work on the job application process early and often. Doing internships during your PhD is a great way to learn about job opportunities, try out a field, and maintain industry connections. Plus you'll make more money than you would doing summer teaching... Keeping an up-to-date resume and LinkedIn and reading about careers on the internet is great too. Many PhD students spend 5 years in a Faraday cage, only thinking about their specific research field: if nothing else, this isn't great for your mental health (ask me how i know...).
Whatever you do, take the time to build and maintain solid competence in general "quantitative skills". This means statistics, basic programming, and basic data analysis (e.g. working with R or Matlab). Ironically, pure mathematicians might be some of the only STEM PhDs who don't have to do these sorts of things all the time in their research, and industry people don't necessarily get this. You can take courses (including ones for undergrads!), build solo projects, practice with stuff like Project Euler, LeetCode, or Kaggle, etc. Depending on what kind of research you do, maybe you can write some Sage libraries to do computations or run statistical models to test hypotheses, etc.
If you pick a sufficiently applied research topic, this all could get a lot easier. For example, having an advisor with industry connections or even just a track record of students with industry jobs would be extremely useful. The industry job market isn't bad for math PhDs even if they study extraordinarily useless things like I do (nobody's launching a startup using perfectoid spaces...), but if you become an actual expert in something useful (even from a theoretical/academic perspective), you could open yourself up to a very elite tier of job opportunities.
Don't sleep on your "soft skills". Mathematicians have tons of opportunities to get really good at technical writing and communication to folks with a wide range of backgrounds. Take the time to get good at teaching, volunteer to talk in student seminars, etc. This is a crucial skill in industry and will be tested directly in interviews.
Job opportunities for math PhDs are certainly not at all limited to the sorts of heavily quantitative tech-y industry I'm discussing below. There's consulting, law/politics/policy, education, technical publishing/writing, just to name a few. You can really leverage those "soft skills" above plus the prestige of a math PhD and your analytical skills to do just about anything!

Specific industries hiring mathematicians:

Quantitative finance: these firms often seem happy to hire math PhDs who can demonstrate strong skills in statistics and competent programming (e.g. being able to write some Python or C++ code to run a statistical model; software engineering background isn't so necessary). "Quantitative researchers", especially at small hedge funds/proprietary trading firms, often work in a fairly academic/research-oriented environment, sometimes even publishing papers. These firms care about pedigrees a lot, so the prestige of your program and university matter (unfortunately!). If you have Putnam or Math Olympiad background, that's very helpful (I've even seen companies ask about standardized test scores?!?). To a lesser extent, having a strong publication record might help. Very few people have studied math directly relevant to quantitative finance (derivative pricing, stochastic PDE, etc.), so doing a PhD in that area would set you up very well. Doing something related to stats or probability is also a big plus, but all sorts of mathematicians are attractive applicants. These firms are willing to invest quite a lot of resources into training and care mostly about finding the "smartest"/most prestigious people. Jobs are 90% concentrated in New York and sometimes Chicago (plus e.g. London and Hong Kong outside of the US).
Data science: here, you're really expected to have some legitimate domain expertise when you apply, at least if you're looking at jobs with the word "researcher" in the name (lots of "data science" jobs are things like database engineering with a fancy name). Jobs involving a large amount of theoretical research on ML models (e.g. at somewhere like DeepMind) are particularly competitive, and you'll be competing with people with machine learning PhDs.   If you can do actual research related to machine learning, even if it's 1-2 side projects/papers, you're in a very good position. You're very strongly encouraged to have built a non-trivial ML project and have the code up on GitHub, e.g. competing in Kaggle data challenges. Programming skills are important, but mostly on the level of being able to hack together a solid model in Jupyter or R. Coursework or a PhD minor would definitely be useful here, and you should at minimum take some machine learning courses at your university. Strong stats/data/modeling experience puts you ahead of a "non-quantitative" pure mathematician.
"Defense"/government cryptography: the NSA and similar organizations (e.g. the "Center for Communications Research") seem to consistently hire very many mathematicians. Background in cryptography or applied/computational number theory is great but definitely not a requirement. These are institutions with deep and immutable sources of funding working over a very long time horizon, and they appear to be rather happy to collect talent and provide space for fairly open-ended research in a wide range of subjects as well as specific domain training. Some general competence with programming is likely helpful but not critical. Having a strong academic/publication record is useful. There are a number of "postdoc" type positions and some "tenure-track" type positions; unlike universities, the NSA actually has the resources to promote a large number of its junior researchers. Obviously, you'll need to pass extensive background checks, much of your work will be secret, and you may or may not ever know what your work is being used for.
Software engineering: this is an enormous and very wide field which appears to never stop growing. Outside of "FAANG", there can be stiff competition among companies for qualified applicants.  Math PhDs are attractive and will likely help you get interviews and possibly bump you up a pay grade relative to even fairly strong undergrad CS majors. But you'll be expected to do the same technical interview as everyone else, which requires you to be able to solve tricky coding problems on a whiteboard. Taking undergrad or grad CS courses at your university is very helpful here, and you'll be expected to be familiar at a minimum with basic algorithms and data structures along with decent fluency in a programming language of your choice. Advanced CS course work helps. Building some sort of cool project/app and putting it on GitHub helps a lot. Big tech companies like "FAANG" (maybe now it's called "MAANA"?) have a lot of competition for jobs but also have better hiring pipelines for PhDs and lots of experience training people without a software engineering background. Startups are more likely to expect you to be able to build something useful right out of the gate. Networking is helpful, especially at smaller companies, so it might be a good idea to hang out at the CS department and make some friends.
Blockchain/industry cryptography research: this is a fairly new industry and will likely look very different by the time you finish your PhD. At the moment (fall 2021), there's a lot of demand for talent but not an incredibly large pool of people with specialized knowledge. This field is developing very rapidly and ideas often flow between academic cryptography and industry, so being able to read a research math paper is a useful skill. Doing a PhD minor or doing math research on the theoretical side of cryptography would make you pretty attractive. It would definitely be worth your time to take a course or two in the subject if this is something you're interested in. Contributing to open-source projects or otherwise participating in the "ecosystem" would be a good idea too. Basic programming skills are necessary, and some software engineering background could be helpful. [Warning: when I started my PhD in 2016, one might have said similar things to the above about the data science industry. Since then, the talent pool has exploded and the growth in demand for industry research has tapered off].
Industry general research labs: e.g. Microsoft Research or Google Research. I've seen such institutions recommended a lot on this site as industry destinations that allow you to keep doing something very similar to academic math research. The caveat here is that the job market at these places is also similar to academic math research - these are basically faculty positions with a different funding structure, and they hire accordingly.

Instead of thinking in vague terms about how your mathematical training can be applied in industry, I think it is much more fruitful to focus on problems in the real world that excite you.  Most math PhDs pursue research problems by letting curiosity guide them.  Things don't suddenly change just because you're going into industry.  Particularly in the current era because of the tech boom, there are tremendous opportunities for bright, mathematically minded people in industry.
It isn't just power point presentations and talking to clients.  Of course there's nothing wrong with that if that's the kind of thing you like.  My point is just that mathematicians have a rare ability to think deeply and get to the core of a technical problem and many corporations are realizing the value they can bring.
If you haven't heard of them, two people to read about are Eric Lander and Jim Simons.  Both started out as mathematicians and went on to become great figures in the fields of biology and finance respectively.  It is notable that Jim started out trading in a discretionary manner and only later incorporated quantitative techniques.  So rather than viewing "industry" as some monolith, think about what problems you want to solve and be confident that your mathematical training will give you a leg up.

I added a PhD minor in computer science to my mathematics PhD. It's helped market me for industry jobs. Every recruiter has mentioned it. One still needs to have the skills (and be able to demonstrate them in the interview) but the minor does seem to get you phone calls.