Does research experience in a different but related field help in PhD application? (Pure Math->TCS) I think the title itself is a good summary of my question.

But to make the situation more specific: I am a double major in math and computer science, and would like to apply to PhD program with theoretical CS as primary research interest. I am well aware that research experience helps a lot to show the committee that one has research potential.

All other academical records of mine are in decent shape so I want to shoot for the top programs. Now I am writing my SoP and here comes the problem: I have only non-TCS research experiences.

What I have in hand are:

1) serious pure math REU (not just expository) with a recently submitted paper

2) technical work (implementing and benchmarking) new numerical algorithm (scientific computing) with tech report

I was planning on doing a senior thesis, but my potential advisor is on a leave so it didn't work out. I went through the old questions and only found one related question. But I guess in my situation, it is worse because I have only research experience in entirely different subject. Although I am very proud of what I did, it is hard to sell them in SoP.

I always thought I don't need to do research in the exactly same area to what I want to do in grad school. Plus trying out interesting areas are fun. But it seems to me now that it was a very bad idea.

Doesn't "I wish to study a_0,a_1 and a_2 in A. I have done research in B and have been a trustworthy cheap labor in C. B and C are somewhat related to A, so I think I will do a decent job in A as well." just sound miserable?

[Thanks for reading - I know it is a bit long. Also I do apologize in advance if this question violates the rules - it is my first question ever. :)]

I'm not an expert, but, from what I know, your experience is quite valuable.  Even if you had computer science research experience, there's a good chance that what you would actually end up doing in a PhD program is far from the domain of your undergraduate research.

If you're still worried, note that theoretical computer science is all mathematics.  I was an applied mathematics and statistics major and am currently taking a graduate course in approximation algorithms--it's literally a graduate course in theoretical side of applied mathematics.  Other theory courses in automata, computation, machine learning, etc are also all math courses, but in the computer science domain.  I actually think that your math/research background, if anything, is a huge plus.

What PhD programs are really interested in is whether or not you have done research in any discipline (preferably math/engineering for CS) and if you enjoyed it and showed an aptitude for it.  You're ahead of the game, don't worry.  On that note, if you know that you want to do TCS research, you could start now.


Could you get a doctorate based solely on a thesis written in your free time? I am a very enthusiastic person academically and I love to gather new (sometimes irrelevant but useful) knowledge about my field in my free time. I am now at the point where I would like to start writing a paper on something in my field of interest and based on my own research.

If my paper is credible, contributes to my field and fulfills the standards that apply to any PhD thesis, would I be awarded a doctorate if I applied for the degree and publish the paper?

As an undergraduate, I don't know much about a PhD program. From what I know, it's simply you and your research that counts. I understand that, in the last analysis, it is the contribution to your field that counts. Am I missing anything?

Just write your paper, as long as the writing does not distract you from your undergrad studies. If the paper has merit, you may be able to present it at a colloquium of your university, at a workshop, or even at a conference. If you put some more work into it and are lucky, you might eventually be able to publish it in a journal. Certainly, your paper will open up more questions than it answers. Now you have a research trajectory that you can follow up and that may lead to a PhD thesis, based on but surely not limited to your first paper.

In other words: There is no reason why you should not start to work on your PhD topic already as an undergraduate, but it seems to me that you underestimate the time and work it takes to finish the PhD. And since PhDs require a lot of work, there are PhD programs, which facilitate the process and in which most PhDs are actually produced. In that sense, writing your PhD outside of the designated framework is a bit like digging a well with a spoon. It can be done, but it is not too efficient. On top of that (as others have said already), at many institutions, the written thesis is only one requirement among others to be awarded a title.


How to discuss job search with your advisor when your career plans differ from what your advisor wants you to do? I'm on the job market this fall (will earn my Ph.D. this spring), and have culled a list of open positions I would like to apply for. I'd like to talk to my advisor about this soon in regards to recommendation letters, as well as for their own information. I'm at a big US research university with an untenured advisor who would also like us all to be at an R1 school or doing research in industry when we graduate. However, my preference is for small teaching schools or lecturing positions, and my list reflects that. Additionally, I have a spouse with a Ph.D., and we will be prioritizing my spouse's job for personal reasons.

I'm among the first to graduate from my group this year. We've all been vague about our job aspirations in previous years, since we've learned that our preferences would often be discounted and it wasn't worth the argument. I don't anticipate anything drastic in this discussion, but I would like to be respectful in conveying the information while still getting across my point that I won't be applying to anywhere my advisor would be happy with.

Any recommendations on how to approach this discussion? Tips on how much to share on what's influencing my/our decisions would be helpful too.

I think a lot of how to approach it depends on the reason that your advisor things you should be heading for a traditional research career.  It's likely one of two things:

Your advisor thinks very highly of your work and doesn't want to "lose" you from the research community.
Your advisor is afraid that not placing students "highly" will reflect badly on their tenure case.

Neither of these is a good reason to choose a career path that you aren't interested in.  If it's the first, maybe you can get your advisor to listen by explaining that you really do want to embrace a teaching career.  If it's the second, then maybe you can explain that it will look a lot better on their tenure case to have an advisee placed at a well-respected teaching institution than dropping out of academia altogether.  At the end of the day, though, it's your choice and not your advisor's.

In either case, however, I would strongly recommend also seeking out other faculty members or external colleagues who understand you better. If you can find such other allies, they will be important in helping back you for the career that you want, whether or not your advisor comes around.


Frustration in getting admission into PhD Computer Science, with background MSc Geospatial Science & BTech Computer Science Am presently really frustrated in getting a supervisor and admission into PhD Computer Science in any University in Europe or US. I recently graduated with MSc Geospatial Science with distinction (cgpa 4.8/5) and I have Bachelors in Computer Science. Ever since I finished my masters have been seeking a PhD admission in computer science in any average schools in europe or US, but all to no avail. I have 3 research papers in top international journal, I even had a research proposal of likely topic relating to Mobile Computing & Mobile GIS, and i seriously think the frustration is weighing me down, please is there something am not doing right or there is any other method of getting a supervisor apart from contacting them through email.

At all decent universities. Ph.D. admission is competitive. Apply following the official procedures to a dozen universities. Make sure your application file really is complete and on time at each. You will not be admitted based on contacting faculty informally.


Difference between Poster Session and Oral Session I'm a student with a question on conferences. What is the main difference between an oral session and a poster session? I want to know the difference in detail.
I can understand the meaning a little bit, but what else can you tell me about the differences in the details?

In an oral session, speakers present their work one-by-one in a series of short lectures (usually no more than 30 minutes, and potentially as little as 10 minutes). Each speaker presents, takes questions for a few minutes, and is followed by the next one. These presentations are usually in a room with a seated audience, in a lecture-style format.

In a poster session, a number of presenters each prepare a poster (in a standard size) and mount them on boards in a large room. For a fixed period of time during the conference, all participants are invited to wander round the posters, reading and asking questions as they deem fit. Poster presenters typically stand by the posters and answer questions as people come by.

For more information, see:

Talks vs. poster presentations: Which is better for advertising your research and building research networks?
How important are poster sessions in conferences?
What are some general good principles for creating a poster for a poster session?


How to make my research productive when my advisors don't give me constructive feedback? Thanks all for your kindly replies. This is my first time to post something on this website and I also read some other posts. I realized that I didn't get the purpose of this website. So I revised my post.

I am a 3rd year PhD student with Mechanical Engineering background. I have two advisors. One of them is a researcher in our department and another is a professor. Both of them had good papers published before. But maybe recently they are not that into research. The citations are going down. I feel like they think about my research only during the meetings. Their typical mentoring style is:

Let the student find research gaps. The student should justify why this problem is important and why people care about it. They never tell you that "you should work on this problem"
The student should be able to seek sources to solve the problem himself/herself, either doing literature search, or talking to other students. They never work side by side with you to figure something out
They meet students every week. During the meeting, they will throw a bunch of questions, such as "why did you use this example to validate your theory?" and "why did you propose this solution?".
When the student ask them questions, like "which system do you think is better for our situation?" and "Should I add this feature to our system?". Their answer would be "You need to find out yourself", "talk to xx", "what do other people do?".
They give suggestions. But they usually say "I am not telling you that you should do this and you need to decide"
When I follow their suggestion but things don't work out as expected, they would say "we didn't say you should do this and you need to think"
They never check your math. If your math goes against their instinct, they want you to explain in pure English instead of going through math.

I admit that my advisors' questions are good. The questions are usually the ones we will encounter during presentation. I also agree that my advisors give me lots of freedom to do research. But sometimes I feel like I don't know how to swim, they just throw me in the water and let myself figure it out. No doubt that my research is going super slow because I had lots of trial-and-error and I did lots of repetitive things. But they are still pushing me to publish paper, which makes me very upset. So how to make my research productive when they don't give me constructive feedback?

I have thought a lot about what I want from my advisors. Here is a list and I am not sure whether or not I am expecting too much:

When finding a research problem, because I didn't have a big picture of this domain yet, I was expecting my advisors could tell me what people care and why.
After deciding a research problem, I would like them to give me suggestions on what kind of skills that I should get or what courses I should take in order to be able to solve the problem.
During the research, when I need to decide something, like what assumptions I should make, I am hoping my advisors can help me decide. They have done research so many years and they should know what assumptions are realistic and what are not.
I hope my advisors are on the same page with me, not only the big picture but also the details. I can find solution or work on math/simulation by myself. But I hope they could understand my solution and my math. I may not always right on math. I hope someone could point out my mistake if there was. I don't want the reviewers to tell me that my math is wrong and to reject my paper.

I'm afraid you may not be perceiving the value your advisors offer in the form of these questions. They are prompting you to resolve ambiguities and to put your project on a solid footing. Rather than telling you what to do, they are providing a more abstract kind of help: identifying matters that you should address. This is no less important to your project.

If they think you are making a poor use of time, then share with them the questions you are actually trying to answer. You just need a consensus on what to work on.


Time overlap between two postdocs? Simply keep two affiliations or quit the first position earlier? My current postdoc position will end in few months, so I started to apply for another postDoc position abroad. Shortly, if I would get the second position, there might be about two months overlap between them.

What is your advise on how to deal with it? What I am considering:

End the first job two months earlier, and start the new one.
Keep both positions during those two months: start the full-time the
second one and continue the first one on weekends, until it "dies"
naturally.

I think the most honest would be the first option (opt#1). However, as my manuscripts were submitted for publication, I will work on them no matter where I end up (likely the second position). The finalization of those manuscripts is my main responsibility, and I cannot be sure about the each review processing time and schedule.

In this case, I would get at least paid for my publication finalizing (opt#2), which would not happen if I will officially quit my first postdoc earlier (opt#1). And I can use two affiliations during the time overlap.

Should I notify my future employer about my postdocs position overlap and my potential plan to keep two affiliations?

My expertise is in ecology, therefore I am transferring neither extra secret information nor some commercial patents between positions.

Around here (in Nordic countries), many institutions and non-academic workplaces include terms about informing the employer of other work besides the one the contract is for. As such, I would be contractually obliged to inform the university that I am also emplyed (presumably full-time) by another institute. Since both of them would have a claim to my full time work, it would be unlikely that I could satisfy that requirement, I would expect them to not accept the situation.

In your case, I would discuss the situation with my current and future superior and give up one position at the same time as starting the other, so that I am only employed once. At least in mathematics, there is typically flexibility to adjust the starting and ending times of the contracts by a month or two, as convenient for everyone.

Also, you can just resign. Out of courtesy, as Buffy noted, please make a clean exit where you finish as much unfinished business as possible and take care that all duties you have (teaching, running a seminar, etc.) are transferred to someone else. Make sure your data or code is available and documented and your collaborators and superiors know where to find it, if this is relevant in your field and country.

At least in pure and applicable mathematics, it is expected for one to continue previous research projects at the new institute. This might be very different if you are hired for a concrete job as a part of a team, but I would guess that most academic jobs allow a measure of flexibility in the matter, as outside collaborations are usually valued. Discuss with your future supervisor if you are in doubt.


Good questions for post-class survey In order to get feedback more quickly, I've seen some teachers run a quick (certainly no more than 5 minutes), entirely optional, anonymous survey for students at the end of each of each class, meeting, or session. The idea is to get feedback on what is working and what isn't on a per-week basis before mid-term or end-of-term evaluations. What are good questions or prompts to use on a survey like this?

For example, I am currently planning to ask something like:

  Any feedback on the BLANK? (e.g., was it rewarding, challenging, about the right about, etc.)"

Where BLANK will be the readings, lecture, and discussion for the class session. I will also ask a general question like:

  "Any other feedback or things you think I should know?"

I've always used the following in ALL of the classes I teach:

What did you like most about quantitative basket weaving? [free text]
What would you most like to see changed for quantitative basket weaving? [free text]
How valuable do you feel your time has been in quantitative basket weaving? [1-10]
Do you have any other comments about the module?

To these I will often add 3-5 other questions which I'm curious about. Sometimes it is to test the waters on new things I'm planning on trying with them or future classes.

The key is to keep it short. ~5 minutes is way too long. If you take more than 1-2 minutes of students' time, you are very unlikely to get many responses.

I usually ask these questions at mid-point and after the final class. However, if you want more frequent feedback, I'd suggest the "one minute paper" where you ask two simple questions (paper, electronically, whatever fits):

What do you think is the most important thing you learned today (or this week)?
What did you find the most difficult point to understand?

One minute papers are useful for constant course corrections while the earlier questions are more to find out how to change the design of the module next time (or next half).


How much code to include in a Physics paper? This question is related to Physics research papers, particularly one that will be coming up for my ongoing PhD research (4th paper in all).

A core part of the research is developing, testing and validating relevant self-written code.  So, my question is how much of the code should be included in the upcoming paper?  Should it be the whole thing, or a written summary of the main components/subroutines used?

If the goal of your paper is to show that your code solves a particular problem or performs a specific task, then it is your responsibility to prove to the reviewers that your code does what it claims to do. The easiest way to do this is to include the code as part of the supporting information for your paper.

However, reproducibility—which is what you're asking about, to a certain extent—is a not-so-simple question when it comes to showing codes do what they're supposed to. For instance, a code that runs one way under one configuration may return a slightly (or perhaps _very) different result when run under another configuration. This doesn't mean that one result or the other is wrong—it just means that this behavior needs to be taken into account when evaluating the correctness of software.

One way to help this is to provide as much information as possible on your testing environment, so that any differences between the system on wich you were working and the conditions the reviewers and future potential users of your code will have can quickly be identified. This would be included, perhaps, as a text file that accompanies the code in the supporting information.


Should I add a person who left my group to my evolved paper? The answer, in theory, is trivial -- add them if you think they contributed -- but there are some nuances...

1) This person (who left about 4 months ago) was a more senior researcher (not Prof. title, in fact below the Prof.) who was in charge of managing a small research group that I'm part of. This person expected to be in the author list of all papers, given the seniority, even with minimal contribution (1h meetings every 2-3 weeks where basically I explained what I was doing and was given some high level comments)

2) We did have a couple of meetings prior to the departure, where the idea (the 'direction', the 'goal') of the research was agreed upon and how it could be tackled.

3) We agreed on a possible solution that I proposed, but this solution did not work (the person is not aware of this, since we haven't been in touch since the departure) and the paper is now quite different. Even though the technique I'm using now was brought up in a conversation, I had already had the idea that it could work so it was not this person's contribution (although the person might think so because it was 'said first').

4) We have had previous arguments over paper authorship, and I gave in and added the person just to be in their good graces.

I do not think this person contributed enough, given that the initial idea was mine, the solution was mine, the paper and experiments were done by me. But I don't want another enemy in academia. At the same time I have negative emotions toward this person because I was on the receiving end of some name calling and threats during a previous argument that affected me negatively.

Thanks for any input.

EDIT: I'm a postdoc.

It is good to minimize the number of academic enemies you have. Purely as a strategic move you might just want to ask them unless it seriously offends you. That seems better than making an assumption one way or another.

How much contribution is enough depends on a lot of things, including field and place, of course. But helping with "high level" direction doesn't sound like it is trivial.

Of course, in asking if they would like to be on the paper you could say that the "paper has evolved quite a lot since you were last involved".

Pick your fights for when they really matter, I think. Especially as a post-doc who wants to move up.


I have to read a paper for my PhD qualifying exams but I don't understand any of it. Does that mean I fail? My committee assigned me to read a paper.  But there is a lot of notation I don't understand.  I tried looking at papers in the reference list and I can't really find anything that explains all the notation and concepts.  I also tried looking for talks that the author gave, but they don't go into as much detail as the paper.

What should I do?  I have no background in this field so I'm not really sure where to start.  I also have about three weeks to figure this out.

Edit: Please stop answering this question because apparently it might be cheating.

If you are unsure of your ability to complete the task, you should go immediately to your advisor and/or the other committee members to get as much help as they can give you.

That is not much of an answer, but I want to supplement it with this: in this case, asking for help on the internet is potentially problematic.  Being assigned a paper and asked to read and understand it in three weeks sounds like a situation which is close to a "take-home exam".  (Maybe it isn't, and maybe you are not understanding the assignment.  Find out.) If so, getting help on the internet on your exam may not be kosher.

Anyway, most graduate students in most graduate programs in most fields do not automatically or easily understand cutting edge papers.  The whole point of the graduate programs is to give them the help and training they need to do this.  So you should be doing what you can to get that help and training, not worrying about whether you are worthy in some cosmic sense.

Good luck.

Added: For those who are confused by the discrepancy between the answer and my comment above: the original version of the question included a lot of content-related information, including the title and author of the paper itself.  I was worried that could lead to getting content-related help from the internet.  The current version of the question is totally unproblematic in that regard.


Use plural or singular first person in abstract of a talk on collaborative work? When writing an abstract for a talk on a topic that I have collaborated on with others, should I be using the plural form, such as

  "We have shown this and that ..."

or is it better to write it as:

  "I will discuss this and that ..."

Use "we", because although only one person will be giving the talk, you are representing the work of multiple people.


How important is your undergraduate degree when it comes to graduate admissions at top universities? Let's say I have an undergraduate degree from a top university in my country which is, however, not renowned around the world. Does this have a negative impact on my chances of getting into a good graduate school?

Next year I will be a high school senior and I am thinking about where to go for undergraduate studies. The two options I have are:

Attending a top programme in my country, Romania, which doesn't make any of the university rankings, but is nevertheless really well regarded in my country.
Trying to get into some top 100-200 programmes in European universities which theoretically give me better chances to get into a top-tier graduate programme. This would be harder for me due to the financial costs (attending any university in my country would be a lot more cheaper).

I want to know your view on whether option 1 or 2 would boost or damage my chances of getting into a top graduate programme in North America or Europe.

The school at which you got your undergraduate degree plays a huge role in your competitiveness on the grad school market. It is hard to give specific advice without knowing what country you are talking about, but purely numerically, for the vast majority of all countries worldwide, having top results and top references from the best university in that country will not get you into any of the top US or UK universities. The gulf between the undergrad programmes can be gigantic, and in some places you will just not develop your potential, almost regardless of how talented you are.

As I say, it is hard to be more specific without more information. Certainly, league tables mean almost nothing, and there are countries whose universities don't feature in league tables but offer solid education. If you live in such a country, then you are still relying on your grad admissions committee knowing it.

Edit: I have seen extremely strong students from Romania doing their undergraduate at top European universities (specifically in mathematics), and then getting into top Ph.D. programs in Europe; I do not recall having seen graduate students at top universities who did their undergraduate in Romania. That by itself does not mean much, after all it is a very small sample anyway.

If I read in a reference letter for a graduate programme that somebody was in the top 10% of their cohort at Bucharest, I would take that endorsement seriously. On the other hand, if you study there, you will have to be aware that the students that you see around you will not be representative of the competition that you will be facing when applying for the top graduate programmes, while if you are at Cambridge, or Bonn, say, then the best in your year are likely to actually be the primary candidates for the best Ph.D. programmes.

My view is that the main advantage of studying at the top places is not necessarily that they objectively offer the best education, but that you will be surrounded by very hard working and ambitious students, and that in turn will push you to develop your own full potential. If you manage to surround yourself with such a peer group at your university of choice in Romania, you will be fine. It's just that at the top places you will not have to look very hard to find such people. On the other hand Cambridge, say, is a much more expensive option than Bucharest. If you have the money, I would go for it; if not, then don't worry and just go to a top programme in Romania.


Transferring / re-applying to PhD programs - Humanities For humanities PhDs:

If you find that your research topic has changed such that you no longer are a good fit in your program, is it possible to re-apply and get accepted into the same program at other schools that would be a better fit?  Can anyone speak to this experience?

When I served on a PhD admissions committee we saw a few candidates like this--people who were in their first or second years at highly regarded programs, but their research statements portrayed more alignment with the faculty in our program. I have two thoughts on this: 1) despite being worldwide, academia feels pretty small in that the professors at your institution might know or know a friend of the professors in the department you are thinking of applying to. Therefore, you should probably have their blessing before applying. It is not uncommon for faculty on admissions committees to reach out to colleagues at the school you are at to find out about you and your research interests. 2) you should probably address the reasons for your switch in a note to the admissions committee or in your personal statement that describes your evolving research interests because people on the admissions committee will likely have questions as to why you feel the need to switch. Admissions committees are trying to find good people to invest in and might be concerned about your ability to make a long-term commitment to the program if you are already switching out of another.

An alternative to switching programs--get a meeting with your POI to strike up a conversation about your latest project. They might be interested in working with you or have students that you could co-author with in the future.


Professor cc to other professors when I ask about the PhD student vacancy. Should I answer as a group or to individuals? I wrote an email to Prof. X to ask about a PhD student vacancy and talk about a topic in one of his papers. He refused to accept me because of his retirement and replied with a warming email. In that, he cc-ed two others that he thought might be interested in me, Prof. Y and Prof. Z. Prof. Y refused because his area is somewhat distinct to mine; Prof. Z hasn't answered yet. I haven't responded to anyone yet. I also want to ask Prof. X's opinion about Prof. T's work, who is actually the one that I want to follow.

How should I respond to this? My feeling is that if Prof. X cc-ed others, this means that he wants to create a group discussion. As a group, everyone should have the right to hear each other voices. Should I reply to the group with a generic "thank you" email?

But if I do that, then I have to send another private emails to Prof. X and Prof. Z. I just worry that this will make me looks sneaky because I don't have the courage to discuss this in public.

What should I do?

The key factor here is that the professor X is retiring.  He is not attempting to initiate a group discussion, he is trying to pass you off to the other CC professors and give you an introduction to them.

The correct way to respond is to send a nice single-addressee thank you note to original professor X only, perhaps also asking your query about Professor T, then send a single-addressee email to each individual Professor listed in the CC.

The key here is that you won't be working with the group, you'll only be working with one of them.


Professor expects me to attend a conference. I can't afford to go,even with 50% funding I am a first year Ph.D. student. During my master's (which was in a different university), a professor (call him M) and I worked on a paper on banking regulations. Apart from the single idea, all of the paper and results were done by me. We worked on it for a couple of months and tried to submit our work at many journals.  All of them rejected it.  Finally, it has been accepted recently at a conference. This conference is not major league but niche.

Prof. M wants me to attend, but I do not have funding. Initially, he expected me to spend from my own pocket for travel and registration (around USD 2,000). Being a graduate student and living in one of the expensive cities, I am living hand-to-mouth. So, I informed him politely that I am not keen to attend due to my inability to fund myself.  He asked me to apply to funding agencies, or else he is willing to fund 50% of my expenses.

The fact of the matter is that I cannot afford even 50% of the travel expenses.  Out of nowhere, this conference comes up and it further cripples my shoestring budget.

How should I proceed?  I am really hesitant to discuss my financial shortcomings with Prof. M, but if I don't, he'll assume that I can afford the travel.

I am really hesitant to discuss my financial shortcomings with Prof.
  M, or else he assumes that I can afford the travel.

I think you need to get past this. You do not have financial shortcomings, you just don't have money. Working on a PhD without family financial support, with existing student debt, and with limited school support is not easy. I respect you for doing it. Take pride in making the hard choices that involves.

With that attitude, explain to Prof. M that you cannot afford any travel expense, and will only be able to go to the conference if your expenses are fully covered. Check any funding sources he suggested, and ask advisors at your current university if they know of any sources.

From comments: Also check with the conference organizers. They may have scholarships or be able to discount the conference fee, and find free or inexpensive accommodations. You may need to work several angles to align the total cost with the available funding.

If you can't get the full funding you need, tell Prof. M. that you regret you cannot attend the conference. At the worst, you will have to withdraw the paper. However, there are alternatives the conference may permit.

Remote video presentation
If anyone from your current university is attending, ask them to present the paper
Prof M. may be able to get a current student or a collaborator who is attending the conference to present it.


Does doing an undergraduate math major make an applicant for philosophy phd programs more competitive? I'm majoring in philosophy at my undergraduate university but I've also taken a fair number of math classes. I've generally found that taking math classes is very time consuming and dramatically limits the amount of time I can spend working on philosophy classes. On the other hand, I feel like I learn a lot in the math classes that sometimes influences how I think about philosophy. I'm a junior and I would need about 4 more math classes for a math major (I have three semesters left before I graduate). For purposes of admissions to philosophy phd programs, should I continue to take math classes and get a math major (in addition to philosophy), or should I just focus on philosophy?

ADDED: I understand that math will be helpful to me in studying philosophy. I am specifically interested in whether it is worth studying in the remainder of my undergraduate career given the opportunity cost for my ability to focus on philosophy.

Recommend getting the math major if you can. (I have double bachelor's in philosophy & math myself, although my graduate school and professional work was in math.)

The undergraduate study is the best time to expand yourself into as many fields as you'd care to take. Generally, graduate school is the place that demands exclusive focus on one narrow area. So the opportunity you have now will not be available later on; and the degree will document permanently that you can achieve in any field you need to work in.

Plus, there's the possibility that later in life you may wish to adjust or switch fields. Having the math degree will allow you to immediately step into programs in any science-related area you wish.

Standing astride two separate fields like an academic Colossus gives you a perspective and options that few others can match, and now is the very best time to establish that.


How to cite webpages in author-year citations? ACM requires author-year-style citations but I have several references such as web pages that have neither an identifiable author and don't state their year of publication. How should I cite such documents?

The ACM's guidelines give the following examples of how to format a bibliography entry for a website:

Website year can be found at the bottom of the website page or by viewing page properties/source to see when the page was last modified.
[1] Harry Thornburg. 2001. Introduction to Bayesian Statistics. (March 2001). Retrieved March 2, 2005 from http://ccrma.stanford.edu/~jos/bayes/bayes.html
[2] ACM. Association for Computing Machinery: Advancing Computing as a Science & Profession. Retrieved from http://www.acm.org/.
[3] Wikipedia. 2017. Wikipedia: the Free Encyclopedia. Retrieved from https://www.wikipedia.org/.

I'm not sure why [2] doesn't have a date. Although that page only covers numerical citations, I think it's reasonable to infer that author-year citations for [1] and [3] would be (Thornburg, 2001) and (Wikipedia, 2017).
APA says to use  "n.d." for "no date"; I'd be tempted to either use "undated" or use the date you accessed the page (but make it clear that's what you're doing, in that case). I've also seen papers that cite websites using footnotes and only put fully citable things like papers, books and theses in the bibliography.


Phd Student here: Where is the line between free labor abuse vs....whatever the "good" version of free labor is? I have an advisor who is very supportive. She responds quickly, offers advice and helpful edits (often on a short turnaround from my own delays), and seems invested in my doing well in the program (Social Sciences program in America).
However, I have, over the years, come to observe that she is prone to seeing others for ways she can "use" them, and has displayed at times disrespect for others' time, etc. So that's made me put my own guard up to look for the same treatment of me.
She helped facilitate my funding for the year (but it's funding for me to do work for another agency), so I think strictly speaking, I owe that agency 20 hours/week, and my other time is meant for degree progress (part of which is receiving mentorship from advisor). But I know that sometimes, much of my week goes towards helping my advisor with her stuff. Her grants, projects, pilot data, administrative work, journal article reviews, etc. The most agitating are absolutely menial tasks like picking up her mail and moving it from one building to another.

So I am wondering...at what point am I just a great advisee, with whom she produces some good work, and for which she will be a long-term (career-long??) mentor and friend and recommender... and at what point am I just being used for free labor?

Edit: The mail pickup is not just junk magazine/letters..but packages and such, some of which may be for my specific studies. But there have been times when, even for packages that are NOT for my studies, I've been asked to do X,Y,Z with them.

I am very sympathetic to your supervisor, so my discussion here will be biased. Good supervisors are not given much recognition nor appreciated much by the system. The system seems to reward poor supervisors by giving them time to do more research.

Could this be a quid-pro-quo situation? It is clear that menial tasks are out of line, and there is a line between exploitation and also asking for assistance. Especially, so she can spend more time to do more for you and her students, providing responsive feedback etc. So the issue is whether you feel appreciated enough right now? And how much time you are spending for her and her agendas. How much of her agenda lines up with your future goals and aspirations? Try to align your agenda and activities so you are not feeling exploited but part of her team.

Despite all this, if your agenda is not aligning, then speak up and try to set boundaries. Realise that your time and access may deteriorate to the bare minimum that other supervisors seem to give? May not necessarily be as retaliation but rather as the result of her time being reduced as well for "menial tasks". Some people really struggle with "menial tasks" and changing focus can be a struggle for those people who prefer conceptual and more abstract work.


Pros and Cons of including less interesting results For the problem I am trying to solve,
I considered 2 base algorithms and devised about 30 variations on each.
I then threw about 2 CPU-years and ran a number of simulations to see how they went.
When that completed I drew alot of graphs and came to some conclusions like:

Variations of the form X do almost exactly as well as the control
Variations of the Y family can be predicted to have unpredictable (and thus useless) results
The d[1,0*] variation is great under these conditions
The d[0*] variation is great under these other conditions

I took the last 2 and made a new algorithm and then tested that and found further useful results.
Now I am going to write my findings up into a paper.
I have 3 kind of results above:

Points 3,4 (and the subsequent improvements) are interesting and will be the main focus of the paper.
Points 1, 2, are kind of nonresults. They are failures, they did not produce anything useful.

For most it isn't even surprising that they didn't.
For others they are a approach by taken on a similar problem in a paper that inspired me to try and solve this related problem.

So should I comment at all about algorithm variations that were tested and found to not be good?
Why/why not?
Pros I can see:
This would help prevent others, not spend time trying them. I have read that it is a problem that in many disciplines (including this one) "negative results are not published".
Cons I can see:
Takes up space, may confuse reader as to which algorithm is the focus of the paper.
It feels abit weird that of the 2 CPU-years I spent testing these, and the considerable time I spent making the tests, I will only tell the rest of the world about 5% of my results.

You could omit the uninteresting 95% in the peer-review journal version, but include them as an appendix in the arxiv.org version of the paper. That way they're available but you're not forcing anyone to read them...


Only one supervisor but two reference letters required I am in the last year of my Master in Physics, I am writing the Master Thesis and I am looking around for some PhD positions. It is something totally new for me.  I have seen that people in general require 2 reference letters.
The problem is that the only person who knows me well is my supervisor of the Master Thesis. I got good grades in general in the courses but I do not think the other professors know, or remember,  who I am.
I do not think I can ask also my professors of the Bachelor (I did it in another University), with whom I had anyway a good relationship, because I finished it three years ago.
So my idea was to contact a professor of my university, or of other universities, and maybe ask if I can do a "mini master thesis" or a project or something, so that he/she can understand that I am motivated and so on, and eventually can write for me a recommendation letter. What do you think?

For the second letter find someone who knows you well enough and has a high enough opinion of your work that they can recommend you without qualification. They need to make a prediction about your future success. Someone recent is better but absent that, an older relationship should work, generally. And someone in academia is better than someone else.
But there is probably no reason to delay your application to do another project. At worst, you could apply in parallel with starting another project as a backup.
Note, however, that my perspective is US, not Germany.


Is this a useful pagination style for a PhD thesis? I write my PhD thesis in LaTeX and I am unsure about one aspect of the pagination.

Usually books have three different page numerations: the frontmatter counts in Roman letters, the main matter with Arabic numbers, and the backmatter in a combination of letters and Arabic numbers, like A 1, A 2, B 1 etc.

The general layout of my thesis follows a book style, where the number of the pages are on the top left corner on even pages and on the top right corner on odd pages. So far, so common.

As I would like to design the thesis so that it gives readers steady feedback for orientation wherever they are in the thesis (this is why I added chapter-wise table of contents and a list of references cited in the corresponding chapter), I have the following question:

Is it good practice to give a feedback to the reader where he/she is in the main matter by adding the total sum of pages to the actual page number, i. e. adding something like Page 13 of 200 or 13/200 to the top left/right corners?

Or may it be considered unnecessary, redundant, distracting or demotivating ("What? Page 13 of this mess and 187 to go?")?

Is it good practice to give [feedback on the reader's relative position in a manuscript by including the] sum of pages[, e.g.,] Page 13 of 200 or 13/200...? Or [is it] unnecessary, redundant, distracting or demotivating...?

Giving feedback on the relative position can be useful, but you needn't explicitly provide additional information (e.g., page 13 of 200), since this information is already implicitly known, e.g., from the physical stack of paper or from the PDF reader.

Pro-tip: Make sure the page numbers match the PDF page numbers.


How to handle multiple versions of a paper (e.g. conference and journal version) A usual flow of a project (in my field) commonly looks like this:

  having a result -> (1) writing a paper (with all the proofs) -> (2) making a conference version -> (3) uploading a full version to eprint -> (4) journal version

Usually, (2) is a shorter version of (1); and (3)
 is almost the same as (1) up to some changes that came up during the process of making (2); Yet, the work on (2) and (3) happens almost at the same time.

The question is how to deal with maintaining both versions (full + short) of the same paper.

So far I have been keeping two separate "papers", but this solutions has its problems: if you make a change to one (say, fixing a typo), you need to remember making the same change in the other one (which you usually forget).

What is the best strategy?

You can change your workflow slightly:

Write a paper with full proofs, polish it, and upload to arxiv.
Prepare the conference version (fairly late). You will use version 1 as your starting point, but you can edit it freely. You can remove proofs and refer to the arxiv version. You can polish the story, introduction, etc. You may spot typos in the arxiv version, but do not worry about those too much. There is no need to revise the arxiv version yet if the typos are minor.
Prepare the journal version. The key thing is that you will use version 2 as the starting point, not version 1. Of course you will now copy-and-paste the full proofs from version 1, but the rest of the text comes from the conference version. Therefore the main matter has already automatically gone through all the revisions that were related to the preparation of the conference submission(s), addressing conference reviews, preparing the camera-ready conference paper, etc.

Now you can simply revise your arxiv submission by replacing it with the journal version, whenever the journal version is ready.

If you follow this approach, there is never need to maintain parallel versions. You will never need to worry about updating version x when you fix typos in version y. People interested in your work can read the first arxiv version early, and your final arxiv version is as well polished as the journal version.

Note that you can start phase 3 as soon as you have prepared the final camera-ready version of the conference paper. When the conference version finally appears, you can already have a preliminary draft of the journal version written, and you can use it to revise your arxiv submission if it seems to be a good idea.


Getting into the Computer Science Industry I am a soon to be fourth year graduate student in mathematics (PhD track). Originally, I wanted to work in academia, but am realizing this is an uncertain path. I feel compelled to finish my PhD degree because I have only two years left (next academic year I must attend as well) and am already pretty far in. But, I want to prepare myself in the meantime for the likely possibility that I will not get a job in academia.

I would like to get into computer science. I did a minor in computer science during my undergrad, so it is not completely foreign to me. But, I do not know what I should do next. Should I be applying for masters programs in CS or is there a better path to get into the computer science industry? I have seen that people have been successful with coding bootcamps and what not, but is this the kind of things that most employers are looking for?

As "computer science" covers anything from front end web development to artificial intelligence so I recommend you try to go in a direction which utilizes your mathematics background. Look into more advanced computer science where your PHD will mean something. In that case you will probably want to take courses rather than the 3 month cram sessions. Those teach you to get a job coding even though you were previously in house keeping. I know because I've worked with them. Some are really good coders. You don't even need a B.S. anymore to do most programming. Do something that is at your level.


Publication issue of a journal article in Elsevier Recently, an article of mine got accepted in an Elsevier Journal. Since it was my first publication, I was not very much aware of the whole publication procedure. After acceptance of the paper, I got a link sent for gallery proof. At this stage, I found many grammatical mistakes in the paper, which I should have corrected during the revision stage. I have to agree that I was late regarding this matter. Yet, I incorporated all the extensive corrections and sent back the proof. The journal manager was helpful. He sent me a pdf file with all the corrections incorporated and asked me to approve the revised proof, which I did. Recently, I contacted Elsevier's customer support, whereby I got to know that the paper is in type-setting. It made me a bit confused because the pdf file I got from the journal manager was already corrected and type-setted.

So my question is how could be the paper in the typesetting stage if I have got a prepared pdf file of the revised proof for approval from the journal manager!! If anyone of you is familiar with this scenario, I would appreciate your feedback.

If Elsevier customer support is the same person as the journal manager, this would be weird. But there's a very good chance that they aren't the same person. Elsevier customer support is likely looking at a high-level overview of what the status is, and doesn't know the details. The journal manager would know it's the 2nd typesetting stage, but presumably the status is still reported as "typesetting" (which is, after all, what it is) and therefore also what Elsevier customer service tells you.

If you're already in contact with the journal manager, ask him/her for the status, not customer support.


Which countries in Europe have teaching positions that do not require doing research? The question is regarding Computer Science field. I wish to know which countries have such teaching positions that do not require doing active research.

Also, what are these positions called?

And, is a PhD required for these positions or Master's is sufficient?

Germany has "Akademischer Rat", which can be permanent or nonpermanent. Depending on the faculty, it can contain various combinations of research, teaching and administration -- permanent "Akademischer Rat" positions in the sciences often consist purely of teaching and administration, but they are rare these days.

Furthermore, the German "Fachhochschule" or "Hochschule" offers professorships which consist mainly of teaching, but they require at least 3 years of working experience in industry.


Additional pre-tenure review requested I teach at a small liberal arts college in the midwest, and am two years through a four year tenure clock (I came in with two years of constructive credit).  After my recent pre-tenure review, the Personnel Committee, in their letter to the Dean, recommended an additional pre-tenure review next year.

My question is: is such an additional pre-tenure review likely to be a condition of my employment?  Can I just refuse, and take my chances at the actual tenure review in two years?

NB: I understand that you may think it unwise to refuse the advice of the committee which will then be reading my actual tenure file (with I assume largely the same members).  My question is about the legal matter.  Do they have the legal right to fire me if I refuse to do an additional pre-tenure review?

Normally an un-tenured faculty member in the US serves on a fixed term contract, usually a year, but maybe more. It isn't that they have to fire you. They just don't have to renew your contract.

It is only after earning tenure that you have a continuing contract and need to be fired for cause. I strongly recommend that you get legal advice or go along


How many resubmits of my thesis support paper can I expect from my sponsor and readers? I'm preparing a support thesis paper for an aesthetic creation for my MFA. How may times can I expect my sponsor and readers to send my paper back to me for edits?

There’s no fixed answer here. Some advisors are willing to look at drafts in progress and pitch in a lot, while others only want to see almost finished drafts. Committee members beyond the advisor would probably only want to see one or two drafts to edit. Further, their comments will be more high-level than in depth.

But the best way to make sure is to ask!


With editor status changes to Submitted to Journal After 5 weeks May I please ask what does this mean. I submitted a manuscript to an Elsevier journal, for 5 weeks it had "with editor" status, now it has changed to "Submitted to Journal". What does this mean?

Elsevier has a page where they explain the submission status [1]. I would assume that "submitted to journal" is equivalent to the status "with journal" on that page. My guess would be that this means that the editor handling your manuscript is being changed. More detailed information if and why this is happening can probably only come from the journal itself, so you would have to contact them directly.
[1] https://service.elsevier.com/app/answers/detail/a_id/12237/supporthub/publishing/~/what-does-the-status-of-my-submission-mean-in-evise%3F/


CV with academic transcript I am going to apply for a visiting research program in Physics. The institute asks for a Curriculum Vitae (a single pdf file) which will provide all my  academic background and professional background info, copies of my academic transcripts, and a Statement of Purpose describing my background and explaining why I feel that the program is right one for me.

I have an "ordinary" CV. Now I am a bit confused about how to add all those things into one such CV. Please suggest me how to go about it. If there are some sample documents,please let me know.

This sounds like a case of the ambivalent comma to me.  In the quote that you provide:

  "Please attach here (in PDF format only) your Curriculum Vitae, which will provide all of your Academic Background and Professional Background information, copies of your academic transcripts, and a Statement of Purpose describing your background and explaining why you feel that this is the right program for you".

it is ambiguous whether it should be interpreted as "Curriculum Vitae (which has your background) and copies of your academic transcripts and a Statement of purpose" or as "your CV should have: academic and professional information and copies of your academic transcripts and a Statement of purpose"

The typical practice would be for an institution to be requesting the second, so I would recommend interpreting it in that manner.  If they allow you to upload only one PDF, just bundle the documents together into one PDF.


Before submitting a paper, should I remove acknowledgement for the sake of anonymity? I have this paper to submit. A professor has given great comments on a previous version of my paper. To submit I need to make it anonymous. Should I also remove my acknowledgement. I think that to mention a name in my paper might be regarded as producing a bias in my favor.

I would remove the acknowledgement. It's better to be on the safe side when you are submitting and do not disclose the data that could be used for author identification. You could always add that part back after review process is over.

If I recall correctly, I have once used a publisher-supplied Latex template that automatically generated pdf file with authors' names and acknowledgement section removed for the sake of anonymity, so the practice of not giving out that kind of information in review copies seems to exist. (EDIT: I remembered well. ACM's sigconf template can be configured for anonymity - in that case acknowledgement section is removed from output pdf file, while in non-anonymous mode this information is retained in the output).


Affiliation in the transition period between studies at different universities I am in a "transition period" shortly after having finished my MSc, and shortly before beginning my PhD. In the summer period the academic life doesn't cease, though, and sometimes an affiliation is asked for at scientific events. I am going to a conference soon, and indeed the organisers need an affiliation.

Is there some rule saying what affiliation I should mention in a situation like that: the past one, the future one, both, or neither?

Be pragmatic. If you need to provide affiliation for, for example, presenting work you have already done, then use the old affiliation. It would be odd to provide your coming affiliation in such cases. Even later when you are at your new place you may want to use the older affiliation along with your new one to show where you did the work and where you are now. If you need to provide contact information not related to work you have done then the new affiliation migt be good. That said, however, it would be wrong to use the new affiliation until you become officially affiliated by a contract (equivalent). From this perspective your old affiliation can be used until the new one is official (find out by asking your new location when can be applied). There is of course not wrong to clearly state that you will be at your new affiliation starting such-and-such time.


PhD in financial mathematics compared to masters This may not be a suitable question for this but I wanted to seek some advice from some of you out there. I came into Florida State University as a PhD student in Financial Mathematics and I am finishing my second year here. I passed my qualification exams and started exploring in areas of Monte Carlo simulation for pricing American options. Although, I have considered putting my PhD on hold and getting some work experience within the field of Quantitative Finance, but I do not have any work experience.

I talked to the director here and he said that I could come back and finish my PhD after if I desired to get some work experience. I am wondering what the difference is in applying as a PhD student or applying as a Masters student since from what I was told working for a 3-5 years coming out of the Masters program would make you just as prepared as a PhD student coming in to the job market depending of course on the experience you get.

Is a PhD really necessary to get a job in the quantitative finance field? Or is a Masters with PhD level training good enough?

I am interested in what you all think. Any comments are greatly appreciated.

From my math profs that have done some pioneering work in the field:

1) back in the days, PhD was required because there weren't MFEs around.  now the PhD is no longer viewed as a "requirement" and many mfe's get hired for roles that used to require a PhD.

2) having said (1), the PhD trains you for a research position in quant-finance.  The MFE will not.  So, different skill sets and different career paths.  Which do you want?  Research / PhD roles, not as many exist today, are very hard to get.  FSU PhD may not open many doors for you to top quant shops or top banks - check this, though.  MFE + programming may be better for you to land at a top place to do quant finance.

3) whatever you do, try to train in lots of numerical methods.  apparently people move up very fast (managing director or similar) because there just aren't enough strong math people in quant-finance.  For instance a prof told me that tons and tons of people at Top Investment Bank X can't actually use optimization correctly.  Strongest math talent go elsewhere - primarily somewhere in academia, so if you claim you're strong in math, you could move up fast.

4) from a head trader on an algorithm trading desk: having interviewed thousands of candidates, he says that there is a stigma attached to PhD candidates, and they are screened, interviewed and assessed more carefully to find out whether he or she even has any relevant skills aside from his PhD thesis work.

5) MFE programs usually have recruitment / networking opportunities

That's all :)


Writing letters of recommendation for more than one student to the same department Suppose multiple students have asked me for letters of recommendation to the same graduate department in the US. Am I expected to explicitly compare the students to one another? Am I supposed to do this in both letters (assuming they haven't been submitted yet)? Do I need to explicitly state a preference for one over the other, or can I just get away with listing relative strengths and weaknesses (for instance, "X is stronger academically, but Y has more research experience and is a stronger programmer")?

Since each individual student is asking for a letter of recommendation it would be appropriate to approach each letter as an individual task and not implicitly cross-reference between the letters by making comparisons. Direct comparisons between the specific students should not be made. It is after all the person or committee admitting the students to the program that will make decisions who may fit the best and their criteria may not be the same as yours. It is, however, reasonable to make implicit comparisons such as ranking each in relative terms to, say, all students you have encountered or some other frame of reference. This way each student is compared to a group and not each other. This is what one commonly does anyway.

I can imagine that you may get a request for more details by the person/committee if there is a difficulty in separating students. You could if you are willing, add a comment to the fact that you are willing to answer any questions that may arise during the process regarding the student in question.


In-class, allowing students to address professor informally ('tutoyer' / 'tutear') Supposing that Dr. A is teaching an undergraduate-level course, what would be the pros and cons for Dr. A to allow students to address himself in an "informal" manner?

By "informal" here I mean using what is employed in a number of languages for informal conversation. For example, this would be tutoyer in French or tutear in Spanish. (Hence, the question does not apply for an English-speaking classroom, but does apply for a French or Spanish-speaking one).

EDIT (clarification, thanks j91): In Spanish, French, German and other languages, the second person singular has two versions: "Tú", "Tu" and "Du" are the informal versions in Spanish, French and German, respectively, and "Usted", "vous" and "Sie" are the formal ones in the same order. "Tutear" is the act of using systematicaly the informal version and, in theory, should be avoided when speaking to a person in a position of authority.

Your question is completely subjective and context dependent. It adresses the balance between formal and informal, which depends on culture, personality traits, position in the academic hierarchy, etc. It's like asking, which are the pros and cons of teaching in full business suit vs Hawaiian shirt?

Some students will feel more confident if the class environment is informal. Some won't. Some won't see the difference. You (assuming you are the Professor) may feel at ease being talked in an informal tone, or maybe not. If you are in a new country or university and need to figure out your way, attend some classes and check which is the standard. Nonetheless, whatever you chose, don't withdraw your authority as Professor.


On citing common knowledge and giving credit to the 'right' authors Suppose that I am reading a marketing book and, while reading a specific chapter, I find like there are 10 footnotes with most of them related to specific examples or specific numbers; in other words, most of the information presented in the chapter are not cited and, judging by the context and contents, such information do not appear to be 'invented' by the author(s) of the book (not primary data). In that case, they could just be common knowledge information related to the field of marketing and business (example: purpose of marketing, strategic analysis, financial measures, etc...)

Since 'when in doubt, always cite', if I want to write about information taken from such book, is it bad if I just cite the author(s) of the book since I am taken the information from their book? To clarify, I am worried that I could be 'giving credit' to people who did NOT come up with such information presented in their book and therefore, I should not cite since it's common knowledge or whatever.

First, the definition of "common knowledge" and I think this answer by Ben Norris from the post that Stephan Branczyk posted elucidated that pretty well (https://academia.stackexchange.com/a/2196):

  If the statement/concept/idea that you think you want to cite is covered (without citation) in the introductory undergraduate textbook(s) on the subject, then it is likely common knowledge in the field.

So next step is to search more than one sources about that specific examples or specific numbers. If that book is the only source that came up with that example/number then you can cite from that author(s). You give credit from where you found the information. It is better to cite than not and might trouble you later on since it will give an impression that those numbers/examples are from your own findings (and which someone might cite that example from you). Because again, 'when in doubt, always cite'.


How to handle a case of unpublished plagiarism? I am a Master's student in a field of the humanities. Half a year ago I was invited to an informal colloquium of mostly doctorands and post-docs who work at the institute I'm studying at. One participant's draft for an essay was the topic of the first meeting.

On working through that draft the evening before, I found five sentences in total that were obviously copied from online sources (blogs, wikis, etc.) without any citation. Due to stress I decided to postpone the decision on what to do with my findings and did not attend the colloquium at all.

To date, I can find no evidence online that the essay has been made available to the public and I do not know if it was ever supposed to be. Its form though is clearly that of an essay (not a sketch or an unstructured gathering of ideas), so that I can not think of any circumstance under which this would not qualify as straight-out plagiarism.

The author is still working at the institute and holding classes.

What is the professional way to handle my findings?

Not all instances of plagiarism are equally severe. So, while your assessment that this is plagiarism may be correct, it looks like at most a fairly mild case of it.

And it may not be plagiarism at all: you say the document being read was a “draft for an essay”. Well, drafts are understood to be incomplete works-in-progress, so having some missing citations (or factually incorrect statements, bad grammar, or any number of other problems we try to avoid in finished documents) is in general acceptable. If the document was explicitly marked “draft”, I don’t see how a plagiarism accusation could make sense. If it wasn’t, then it would depend on additional contextual details - I guess we’d need to know what your description of the document as a draft is based on.

A related question is, what benefit would come to the writer of the essay from passing off the quoted sentences as their own? Was the work read in that meeting graded, or was there some other material benefit they could have hoped to gain? If there was, that could make an intentional deception more likely as a possible explanation.

At the end of the day, what you’re describing sounds sufficiently mild that I don’t think you should get too alarmed that the person is “still working at the institute and holding classes”. You can complain to their adviser or some other person in charge if you are genuinely concerned, but I would expect that this person would, at most, be told to be more careful in the future, but would not suffer (and do not sound like they deserve to suffer) any more severe career consequences.


What is a delicate way of identifying key insiders? I am beginning a doctoral program this Fall and have been receiving helpful advice from a recent graduate. While this graduate has mentioned a key mentor that will likely serve as my primary advisor, I am curious to know if there were other mentors instrumental to his success in the program.

What might be a delicate way of inquiring who these people are?

EDIT: I was initially afraid that having someone identify the "good guys" might imply that I reckon that some are "bad." But this is probably overthinking it, as one comment pointed out.

Let me just emphasize one thing: there are very few objectively good advisors/mentors. It really depends on both the mentor and the student’s personality. Some prefer a more hands on approach while others like to be independent. Some are motivated by harsh criticism while others need a more gentle approach.

To conclude, I think what you’d want to ask is more “what is Professor A like?” rather than whether they’re good/bad mentors.


"other spoken languages" in graduate applications I am noticing that many graduate applications require a "primary spoken language" and "other spoken language".

For the "other spoken language", do they mean you must be proficient in writing, speaking, and reading? What if you can only do one (and you may not even be great at it...)? Does that count?

EDIT What if I do not know any of the jargons used in my study in that language?

If they ask about "spoken" languages then it is about speaking, not about writing and reading.

This should not be a surprise depending on the area of research you are studying. It could impact those you will be working with or it could involve your research subjects. For example, if you want to study cross-cultural team dynamics specifically between fracophones and anglophones and you only speak English and you cannot communicate at all in French, then you are going to have some difficulty with your research.

If they do not ask you to qualify your speaking ability, then do not worry about it. If they do, then answer appropriately.

So, in short, yes, if you can speak one other language even if not well, it does count.

As a side note, I know plenty of people who speak zero other languages.


What can a student who is already studying hard do to improve low grades? I have been studying hard for the past year and a half (it's my second year of Computer Science) while not getting remotely close to the desired results. I passed my first year with a 7.2 GPA (5 (failing grade) - 6 - 7 - 8 - 9 - 10 (best)) which is very low given that I want to master in applied maths or an area revolving around that.

I'm doing 6 hours a day Monday to Thursday, 10 hours a day Friday and Saturday, and on Sunday I try to rest a little. I rest every 30-45 minutes for 5-15 minutes. Now, I'm isolating myself, sacrificing my social life, among other things. I'm feeling angry at myself every time I get the exam results or when a deadline passes and I haven't finished the lab/project.

I even started eating healthier because I thought unhealthy food was the problem.

What can a student in this situation do to earn better grades?

P.S: I do like what I study, no matter how angry I get, I know I will wake up the next day and head to the library/school.

Don't panic. The fact that you're still enthusiastic about your studies suggests to me that you can likely fix the problem, whatever it is.

If you're having trouble with just one or two of your subjects, then there's probably a gap in your background knowledge. Try to figure out where that gap is. Right now it may feel like everything in those subjects is difficult, but I suspect if you look hard, you'll find out there's just one or two small gaps in your knowledge, and that's something you can remedy. Perhaps you can take a lighter course load next semester to give you time to focus on the areas you're having difficulty. Once you figure out what the gaps are, you could either take the appropriate course, or teach yourself. This strategy may delay your graduation by a semester, but it could be worth it.

If you're having trouble with most of your subjects, then I suspect you're not studying very efficiently. You may be working hard, but not using your time well. Unfortunately, I don't have specific advice on how to improve your study habits, but there are lots of resources available for this kind of thing, and others here may be able to point you to those resources. Again, it might be worthwhile to take a lighter course load while you practice your new study habits.

Talk to your student advisor about your problem. If you think it's a gap in your foundational knowledge, t may be worthwhile to pick one problem that you found particularly difficult, and ask the instructor (during office hours, not in class) to go through it in gory detail with you to help you figure out where your knowledge gaps are.

Also, there is probably a student centre or something like that at your school, where you can get advice on how to improve your study habits.


Does the journal Physical Review require an institution in the affiliation field? Does anyone know what politics or rules the journal Physical Review follows in regard to not writing an institution in the affiliation field?

To my knowledge, there is nothing preventing someone from submitting a paper to a journal without a formal institution as part of the reader's address. You might have to write something in a web form such as "self-employed" or something similar if institution is a required field for submitting the form.

However, it is fair to say that under most circumstances, if you are submitting a paper without institutional affiliation, it will probably be subjected to heightened scrutiny relative to a paper with more "traceable" affiliations. Unaffiliated authors are usually treated with some skepticism in the academic community, and papers by such authors tend to to be viewed as "crackpot" papers. (Granted, having an affiliation does not preclude the possibility of submitting such papers, but the likelihood is somewhat lower.)


Why do many talented scientists write horrible software? I am a software engineer and I have been working with people with academic backgrounds for several years. Many times, I've noticed that (even otherwise brilliant scientists) produce code of extremely low quality (unless their background was precisely Computer Science).

Since those people are very good in doing their research - and eventually obtain remarkable results - it seems they are clever enough to write decent code. Is it just that they don't think it's worth the effort? Plain arrogance? Lack of time?

Examples

It seems to me that in academia the most popular languages are C/C++ and Python (neglecting MATLAB and other vendor-specific languages). The language where I have seen the most amazing pieces of junk is actually C++. The main points are:

Really, really naive C++ code. They claim they chose C++ over Java/Python/whatever because "it's faster", but they new everything, even an array of 3 floats that is deallocated few lines later, where 3 is known at compile time.
They have learned pointers from C and they use only them.
Some of them (not most of them) have read some random blog posts about OOP and now put virtual everywhere, using abnormal levels of abstraction.
They are convinced of pointless optimization choices.
They lack proper memory management.
They copy/paste massive amounts of code from project to project and within the same project as well.

And in this list I am omitting the problems with the process, rather than the product. Scientists use:

no version control,
no automated builds,
no documentation,
no software process at all (neither agile, nor traditional waterfall).

The workflow is:

  I devise the algorithm, I write it as a massive 10k LOC piece of C++ and I click build somewhere.

As this assessment could be probably biased by my own experience, I have inspected some open source projects run by researchers (and maybe a few software engineers) and cited in many important papers. Virtually all of them were:

crashing on corner cases,
had ugly GUIs,
and the code - in my opinion - was ready for a complete rewrite.

I'll have a go at this. This is mainly my personal view based on my use and implementation of academic software. Like many of the comments already mentioned, I don't think bad software is specific to or even more frequent in  academia. That said, I think there are a few reasons why it occurs that are specific to the field.

Software is not a priority

In academics, the key performance indicators are all about paper publications. Software, while highly useful in my opinion, has very little value. More often than not, software implementations are side tracks or proof-of-concepts at best used to bump up the citation count (ofcourse exceptions exist).

As a software engineer, I am sure you are aware of the knowledge, time and effort required to produce quality software. Given the publish or perish mantra in academia, this time investment is often spent writing a new publication. It's a risk-reward consideration.

My personal point of view is that quality software is very important. For example, often, the first step in comparing a new algorithm with previous state-of-the-art is reimplementing the state-of-the-art due to a lack of implementation. Obviously this introduces a time delay and can cause a series of bugs. That said, I think in general little will change until software gets valued by performance indicators somehow.

Researchers are not programmers

Most researchers have little or no programming experience, though YMMV depending on the field. I think it's fair to say the majority of researchers learned to program by themselves when confronted with problems where they need it. Superficially learning a language is typically not the problem, but you need more than superficial knowledge to produce quality software (choosing data structures, design patterns, deep knowledge of the language, ...). This is a hurdle for self-taught programmers without a computer science background.

Another problem that less experienced programmers face is not realizing when refactoring is necessary. You can find countless pieces of poorly structured software. In research, this is a natural consequence of iteratively implementing while designing an algorithm which evolves into a monstrous piece of software full of hacks. That monster might still do what it was meant to, though, if you know exactly how to ask it nicely. For many researchers that's where the story ends: get the results and put the beast away forever. It often takes a serious time investment to wash the monster prior to talking it out for a walk in public. This is not always worth it.

Trends in machine learning

My field is machine learning, in which software is being valued increasingly. Examples of this trend include a growing software repository and a position paper by some big names in the field. I am very happy with this evolution, because quality software allows the field as a whole to progress faster and increases reproducibility.

A current patch to the problem is being able to publish papers about peer-reviewed software implementations. I know this is possible in machine learning and statistics. Such software is usually of higher quality.


Is it appropriate to acknowledge StackExchange in my MSc thesis? So, I'm just now finishing my master's thesis. When thinking about who helped me aside from my official supervisors some of the StackExchange fora (especially Stack Overflow and TeX) came into my mind.
I asked some questions in these fora but most of the time I was just looking for information in older posts (and finding it most of the time). Now one the one hand it feels weird to me to acknowledge a site I browsed to, e.g., find out a certain programming command (like I would not really think about acknowledging Wikipedia). But on the other hand I am really grateful for some of the quick and professional answers I got from StackExchange users.

So what do you think, should I acknowledge StackExchange in my thesis?

Your acknowledgement is a place where you are free to thank anyone or anything (I have seen dogs being thanked) you think deserve mentioning. To acknowledge StackExchange is highly appropriate since it is a community driven open site where help can be obtained to solve problems from many different perspectives.

In my PhD thesis, I, perhaps a bit over-ambitiously, thanked Donald Knuth for creating TeX. Had StackExchange been in existence back then, some of the terror of writing my thesis in plain TeX would have been eased and it would have been placed along-side Knuth in the acknowledgement.


What are risks or disadvantages in uploading to figshare or related services? Figshare lets researches publish figures and data for long-term archival and public access. I just found out about it and find the idea sympathetic. See for example their F.A.Q..

What risks are involved with publishing my data and figures at figshare? What are the disadvantages? Did anyone actively decide against doing so, and if so, why?

Well, some journals have a "no prior publication" rule in place. These rules often preclude publishing anything that has already appeared in online databases that are freely accessible, so publishing figures in advance of a publication in a medium like this could be construed as violating such a policy. Since anything publicly posted gets published under a CC-BY license, so this almost assuredly counts with those journals as "prior publication."

In addition, anything that could be considered "confidential" or "proprietary" information definitely should not be posted on such a site until you have secured the necessary permissions from project partners and affiliates. That would also apply to "private" hosting of the figures.

Your own university or research institution may have policies that have some bearing on the ability to use such sites, so check with them as well. Better safe than sorry.

(Note: Those cautions aside, I do think it's a clever idea, and I hope it can catch on!)


What year to use when citing second editions of books This seems like a silly question, but I can't find a clear answer online. If Dr. Brainiac's Big Book of Science was first published in 1974, but I have the second edition, published in 1996, should I cite it as (Brainiac, 1974) or (Brainiac, 1996)?

I realise that this is the sort of thing that might come down to the policy of the journal, but it happens that the journal I'm submitting to doesn't offer any specific guidelines on this particular topic, so I was wondering what the standard practice is, if there is one.

edit: for clarity, of course I would mention in the references section that I was referring to the second edition. I guess it would look something like this:

Brainiac, Q. Big Book of Science. Aperture publishing, 1974. (2nd edition 1996.)

or

Brainiac, Q. Big Book of Science, 2nd edition. Aperture publishing, 1996. (First published 1974.)

Where in the first case the in-text citation should be (Brainiac, 1974), and in the second it would be (Brainiac, 1996). The question is which of these is considered the best, or at least the most usual, way of doing it.  In my particular case I'm citing a specific fact that is almost certainly in both editions, but of course I can't be sure.

In this case I would say Be Honest: only cite the references that you have read. Even if you have read the "rev 1", you might be (wrongly) citing as "rev 2" a sentence from "rev 1" that was removed in "rev 2"...

It might be obvious, but sloppy mistakes like that happen


Changing research field as PhD applicant I am a CS undergrad currently thinking about my post grad application. I have a couple of questions.

So far, I published 2 first author papers, one in SODA, and one in VLDB. However, they're on two totally different topics (One on compression algorithms [theory], one on graphs [DB related])

I recently (last year) started getting interested in Machine Learning and specifically reinforcement learning, and have been spending a whole lot of time on it. I decided I want to apply to a PhD in that field.

Would my unrelated previous research papers add any positive thing to my application, or would it just be better to apply to a PhD that relates to my my published papers?

Would my unrelated previous research papers add any positive thing to
  my application [...]?

Your past research will certainly strengthen your application, even if it's not in the subfield you aspire to study in graduate school. Grad schools look for students interested in and able to carry out research, and a lot of those skills are transferable across disciplines and certainly subfields. It's also perfectly normal to change your research interests during undergrad (or be constrained in topic choice by what faculty in your department are working on or any other number of factors).

  [...] would it just be better to apply to a PhD that relates to my my
  published papers?

With the caveat that you should feel sufficiently committed to your new field to not lose interest in grad school, you should definitely want to apply for the PhD that you want to do rather than the PhD you think your CV makes you look like the best candidate for. You'll be in a program that better matches your interests and you'll do work that's more meaningful to you (and hence often much better).


Why would a small paper (10 pages) with an important result take over a year to get accepted? Below is an example that I had in mind, but my question is more general: does the reputation of some authors cause their papers to be prioritized for review sooner than other important papers, e.g. the paper "primes is in p" (link below), which was written by unknown, Indian grad students?  In this specific example, a year seems like a long time to review an important -- and very short -- paper.

http://annals.math.princeton.edu/2004/160-2/p12

Milestones

Received: 24 January 2002

Accepted: 21 March 2003

Published online: September 2004

There can be many reasons why a paper takes a long time to be published. The `official' reason is that the paper needs to be carefully reviewed and considered before the result can be accepted for publication; the more prestigious the venue, the more care is taken in reviewing the results. However, there are other factors that come into play

This is in my view the most important: reviewers take a long time to get to finish their review, especially in high-quality venues. One should remember that reviewing is unpaid labour that academics provide to the academic publication industry for (what is apparently) the right to publish. There is no real incentive to review; there are meta considerations, like standing in the community, that depend on how much you do it, but I know several successful academics that do the bare minimum.
Good results in bad packaging: even a groundbreaking set of results can be poorly structured, which would result in delay in publication (if not outright rejection).
Politics and names do have some (minor) effect: having prominent authors/friends on the editorial board can result in a slightly faster turnaround. However, I have not heard of cases where editors have unethically accepted the work submitted by their friends, or rejected/dragged on the review process for the work of competitors.


Publication process in a journal I have a paper with revise and resubmit in a journal of Elsevier. In one part of the mail, the editor wrote me this:

  If you choose to revise your manuscript it will be due into the
  Editorial Office by the Aug 30, 2018.

  The deadline is given only because we want you to give a high priority
  to revision of this submission. We assure you that a revision will be
  reviewed normally even if it is submitted after the deadline.

I do not understand this deadline. If the paper is reviewed normally even if it is submitted after the deadline, why do they put a deadline?

So is it possible to submit the revised version after the deadline?

Journals have deadlines for publication. Everything that will go into issue X needs to be ready by date Y. If you meet their deadlines you increase your chances for early publication, otherwise you may be put off.

However another problem is that while the reviewing is "normal" the editorial process of choosing papers for issue X also comes in to play. The other language of the publisher's reply might imply conditional acceptance if returned by the date given. After that, unless you already have acceptance, it might become more tenuous.

Basically, what they are saying is "We have constraints here. Please help us meet them." It would be good, if not essential, to honor that.


How can women improve their chances to get postdoc positions in computer science? I am planning to apply for postdoc positions in computer science (CS) departments, preferably in the US or Europe. I have searched several computer science departments/research groups at different universities to see the current projects and Postdoc researchers. I saw that most current postdocs are men. So, the following questions came into my mind:

Is it because either women did not apply for the position, or were the women's applications unsatisfactory to get the position?
Is it true that women do not have much interest and hence success in CS than men? or lets say are men in general better in CS than women?
Although it is unethical, is it true to assume that the CS members have tendency to hire men more than women in reality?
If answer to Q3 is "yes," what would be the most effective motivations to encourage
the hiring of women postdocs in CS?
For women who have recently finished their PhD's, is it better to apply for academic research fellows or industry research positions?

I can only answer for Europe, but I assume JeffE is going to provide high-quality info for the US anyway.

  I saw the most of current postdocs are men.

With some exceptions, this is unfortunately very true across Europe. And not only post-docs. It gets worse the higher "up" you go in hierarchy.

  1- Is it because either the women did not apply for postdoc or women's applications couldn't make the CS community or individual faculty members satisfied?

In Vienna, where I did my PhD, we simply received basically no female applications. It was not a question of my professor not being satisfied with the female candidates - in the majority of cases, there simply were none.

  2- Is it true that women do not have much interest and hence success in CS than men? or lets say are men -in general- better in CS than women?

I am sure that women are able to do CS just fine. We just "lose" them some time during school. How this happens is a question of reasonably heated debate (gender studies etc.), and I do not feel qualified to answer it competently.

  3- Although it is not ethically feasible, is it true to assume that the CS members have tendency to hire men more than women in reality?

This will surely be true for some individuals, but by and large the official university policy in most places is that "equally qualified" female candidates should be hired over male ones, and this actually seems to be the case. In general, as most universities are pretty desperate to increase their quota of females in higher positions, being a top female researcher will actually make it easier for you to get a strong postdoc or junior faculty position in Europe. Edit: I should make clear that the last sentence is based on personal opinion and anecdotal evidence more than anything else.

  5- For the women who newly finished PhD, is better to apply for academic research fellows or industry research positions?

Follow your heart. I don't feel gender should play into this decision. Anyway, in Europe there are preciously little industry research positions, so for the most part it's either academic research or industrial practice over here.


Guidelines for changing terminology introduced by self? The topics that have been in my research focus recently have problems with consistent naming and terminology through time and papers, so I might perceive it as a greater problem than I would normally, but still. I am a PhD in Computer Science, Image Analysis -- so while there's a lot of standard terminology, a lot of state-of-the-art terminology has not settled on unique names yet.
Until now, at least all the papers made by the same author/research group/under the same adviser (same last author) I've read have been consistent in their terminology (at least the one describing their main contribution). But, the latest article from an interesting author has changed his own terminology on purpose (he says so explicitly himself).
What would be a good reason for terminology change within the scope of one's own work? It's not merging work with somebody -- in fact, the article I'm talking about is the first standalone work from that author after publishing with an adviser during PhD. From my point of view, it just makes it less findable: the key words changed, which might in turn cause less people to read it and in the end cite it. How can this be beneficial?

EDIT I am actually interested in a general set of guidelines for when changing one's own terminology might be beneficial, but let me add the details of the case that inspired the question. It's the terminology to describe a data structure constructed for an image:

In the first 4-5 publications (during the PhD, before publishing the thesis), the terminology was new, the name was suggestive and conforming to the other terminology from the same filed, but still different than other data structures with similar purpose and of similar format.

The latest publication (1 author only) takes over some terminology from a related work, and introduces ambiguity. That terminology was first used to regard components of a similar, but still different data structure. No attempt was made to prove that those similar structures were equivalent (and I think they're not -- still didn't get around to proving that). A new term was created, derived from the terms for components.
Now to me a stronger suggestion is that similar but different data structure used as a basis for terminology, rather than what it was actually meant to describe.

I also admit to doing this. The reason was twofold. Firstly, the old terminology clashed with existing terminology for concepts that I also needed, and secondly, the new terminology better reflected the concept that I was studying. It is also difficult coming up with a name for something when you invent it. But concepts need names.

There are obvious pitfalls to this approach, such as making papers harder to read. It is beneficial in the long run, because the accepted terminology (assuming that the newer terminology becomes the accepted terminology) does not clash with other terminology I need to use.


List of majors offered by each US College/Univ Where can I acquire/obtain a list of majors offered by each US college? Preferably in excel?

The National Center for Education Statistics (NCES) runs a database called IPEDS that contains data about virtually all institutions of higher education in the US (any institution that receives federal financial aid funds is required to report to IPEDS.)  Along with information about graduation rates and student demographics, you'll also find a list of all of the degree programs offered by each institution.

You should also be aware of CIP codes.  These "Classification of Instructional Programs" codes provide a standardized system of numbers to identify college majors.

It's possible to download data from IPEDS.  The system is somewhat cumbersome to use and it can be slow, but there is an immense amount of available data and you should be able to get the CIP codes of the programs offered at all of the thousands (over 7,000!) of institutions in the US. If you haven't worked with IPEDS before, I'd encourage you to find someone who is an expert to help you understand the system.

IPEDS also has a user-friendly "College Navigator" search that can be used to search for a particular college and lookup its data.