Leaving PhD in the 6th year and apply for a new PhD program I am a 6-year PhD student in computer engineering in an Asian country. My research field is Bioinformatics which was really hard to me in the beginning, but now, I love it and I have much experience at it.

Last year I was an intern in the bioinformatics department at a high-quality university in the United States. Since then, I have become disinterested in pursuing my PhD in computer engineering. Actually, most of the times I think of leaving my PhD and applying to a bioinformatics PhD program in the United States.
However, I am worried if leaving my current PhD program in the 6th year will appear as a negative point on my CV when I am applying for a new PhD in bioinformatics.

If I leave my PhD, do I have any chance of getting admitted to a new PhD program in bioinformatics?

EDIT: Also, I am worried about the letters of recommendation. If I leave my current PhD program, of course I can't get any recommendation from my PhD supervisor.

You don't give us much to go with. What you are proposing sounds pretty dramatic, and it potentially has a lot of factors going against you: Based on your description, it seems that 1) you are abandoning a Ph.D. very deeply into it; 2) you are parting ways with your adviser unamicably; 3) you are applying to a highly competitive program in the United States; 4) you may not have publications. The one thing potentially weighing in your favor is the fact that you've interned at that university: If your adviser-to-be is willing to commit to advising you and supporting you financially, it may override the other concerns in the eyes of the department.

Someone above suggests applying yourself to completing your Ph.D. and then seeking a postdoc in your dream area; that could well have better odds of success in the long term. Ultimately, of course, there's no way we can predict what the outcome will be. I'd suggest that you be sure you are being completely honest with yourself about the reasons for the situation you find yourself in, and what it is you are trying to accomplish by starting anew.


Publishing the same results at multiple conferences I presented my psychology thesis at an undergraduate conference, an international conference in France, and am now interested in submitting it to be presented at the 2013 APA conference in Hawaii. Since it's been presented to different audiences at each presentation is this unethical? Also, the first two presentations were simply poster sessions. If I elect to present the paper as a 10 minute talk rather than a basic poster session, would that be unethical?

Ask the conference organizers.

The ethics of multiple presentation, parallel submission, and multiple publication vary from field to field, and even from venue to venue within any field.  Only the conference organizers (aka program committee or steering committee) can answer definitively whether they would encourage, allow, accept, discourage, or forbid speakers to present results that have been presented before.

If the conference has an explicit call for submissions, read it carefully; it may include specific language addressing this issue.

One thing you should absolutely not do is attempt to hide the fact that you've presented this result before.  Do not seek forgiveness instead of permission.  Even if the submission policy clearly allows you to submit a previously presented result, you should make its presentation history clear when you submit.  If the organizers accept your submission even with this data in hand, you're clear!


Is it legal to accept this type of scholarship while on an F-1 visa? So here's my situation: I am a student on F-1 visa who has been offered a scholarship from another university (not the one where I am studying) for a week long workshop in a subject that I research. This scholarship is not classified as employment, and it is just a scholarship / reimbursement money. Is it legal to accept this scholarship?

It is quite legal and has been done many times. Students attend conferences and get reimbursed for that.


Is my code my intellectual property or the university's? I'm an undergraduate researcher. I wrote a script to solve a problem when I was working on our previous research project. I was unpaid at the time.

Now I am paid for my research and am updating my script (so that it runs better and is more easily accessible to my research group and easier for them to maintain when I leave.)

Who owns my code? Me or the school? Does it matter that I was unpaid (essentially doing the research for fun) at the time I initially wrote the code?

I'm fine with my research group using it in the future, but I want to be able to use it in the future as well when I move onto graduate school.

My current plan is to really build on it and make a really nice suite that would be applicable to both what we're doing now and what I would like to be doing in grad school, but I don't want to do it if I'm going to run into legal hot water.

As noted in the comments, this really depends on university employment policy.  It is further complicated by the fact that you were a student but not an employee when you did the first work, and so different policies might apply.  If you wanted to commercialize the software yourself, you'd probably need to get lawyers involved and everything would likely become complicated and sticky.

Given that your stated goal is to be free to work with the software in the future, however, the easiest path is probably to simply nullify the question by getting the software released under a free and open software license.  This is typically quite easy to do with early-stage academic software, and (de facto) usually just requires consent from your professor.  This is a win-win solution: you and the university both get credit for the software (no matter who owns it, your authorship is never in doubt), and you and everyone else in the world gets to make use of it and improve it, no matter where you may go in the future.


Is there any academic advantage of being the submitting author of a paper? It is well known that in most fields the order of the authors in a paper indicates the authorship, being the first one the best.

Independently of the order in a paper, does being the person that submitted the paper (not the corresponding author) gives any advantage, intellectual property, or academic reputation? If it does, is this field dependent, or academic culture dependent?

No, there is no academic advantage of being the submitting author of a paper.


Do you think a computational math student should go to student seminars about pure math? I am a first year graduate student with thesis topic on computational math, in particular inverse problem. Based on what I know, this area requires a solid background of functional analysis and numerical PDE.

Currently I am wondering what kinds of seminars I should attend. My department have weekly student seminars on analysis and theoretical PDE. Basically students presenting their work/research output to their supervisor. As this post and the great mathematician Terence Tao suggested, attending talks and conferences in different areas is beneficial.  Hence I will try to attend other numercial analysis groups' seminars. But I was wondering whether it's helpful for me to attend those pure math seminars about analysis or PDE.

Though I found those seminars are interesting, my main concern is that being lack of suitable background, I may only be able to understand a small part of the seminar (a PHD student working on theoretical PDE once told me he can't understand his group-mates' seminar until his third year). The other concern is I will have less time for my own research . So in your opinion, what kinds of seminars I should attend? Shall I attend seminars in other departments (say physics or engineering) regularly?

You're not going to be able to determine which seminars are useful to you without going to some of them. And yes, some of them will be wastes of time. But I've gone to seminar series that should be extremely topical to me that never benefitted me directly, and some from peripheral departments that turned out to be very useful - as mentioned in one of the comments, even if you don't take away anything from the lecture, it's useful to know people. One day, you might find yourself in need of a graph theorist or some such, and it's helpful to have met them once, or be able to say "I saw your talk..." in an introductory email.

Also, selfishly, from an applied field, having applied participants in more theory heavy fields is helpful for teaching theory students how to present their topics to people outside their narrow subfield.


Do sample solutions increase student 'productivity'? I'm not 100% sure whether this belongs here, but since I am a PhD student (teaching) in the TCS/Algorithms department, I'd like to know what fellow, maybe more experienced, teachers think.

The question at hand is whether we should offer sample solutions to all our exercises for, e.g., a Data Structures & Algorithms lecture.
I am convinced that this would be a highly beneficial service for our students whereas my advisor is against it. Here are the pros and cons that we came up with:

Pros:

Students have access to high-quality answers when they don't understand something.
Our expectations on verboseness, conciseness, depth of proofs, etc. can be communicated more clearly.
A student can individually study using the solutions and is not forced to attend the tutorials if this is not his preferred style of learning
We (the professor/tutor) have a clearer idea of what solutions to expect since we have to work the problems ourselves.
The tutor (me) is more free in the design of the tutorial. Without sample solutions, the tutorial basically boils down to writing the sample solutions on the blackboard. Otherwise I can't be sure that everyone has at least seen the correct way how to solve it. Little interaction is involved.

Cons:

It costs time and/or money.
Students may stop being engaged in the exercises since they know they can always look at the sample solutions.
Students may stop coming to the tutorials.
We can't reuse exercises from past years since students might have access to (and use) past sample solutions.
If we do it once, the students might expect we do it for every lecture.

To be clear, in both cases the students are expected to solve the exercise sheets on their own, and they will be graded. I'm merely interested in what to offer after this has happened.

I think that's all. Optimally, I would like to find some kind of empirical study that proves that sample solutions increase the "productivity" of students. Data always wins. However, so far I couldn't find anything like this.

To discuss the points mentioned above, my general opinion on these matters is that if we can offer more services using little work, we should always do it. If someone really misuses it (as stated in the cons), he or she will notice that this is the wrong approach the latest in the exams. My advisor, however, wants to minimize the time spent on lectures and have me rather do the research relevant to my PhD. Since I have to do the solutions anyway, the overhead for providing a sample solution is maybe 2-3 hours/week.

What do you think?

I have three types of exercises in my algorithms classes: homework problems, exam problems, and discussion problems.  I'm not entirely sure which you're asking about.

I release detailed solutions and grading rubrics for all homework and exam problems, in part for the advantages you list, in part to speed up grading, and in part to better calibrate my own expectations for the students.  (If it takes too long for me to write up the solution, the problem is probably too hard for them.)  I take them all down again at the end of each semester.  I don't actually mind if students have access to my old solutions—as long as they write in their own words and cite their sources—because homeworks are only a small part of the course grade.  (Students who are stupid enough to submit my old solutions verbatim, typos and all, are not quite publicly fed to the wolves.)

On the other hand, I deliberately do not release solutions for discussion problems (which we discuss in, you guessed it, discussion sections) because the solutions are not the point.  The point is to practice finding the solution.  I know students are adults, but it takes a lot more discipline to practice hunting when someone just regularly hands you the meat.  Also, some discussion problems reappear later on my exams.

But this is really an individual choice.  I know plenty of algorithms instructors who don't give students solutions, and others who hand out solutions on paper but don't distribute them on the web, and others who distribute them on the web but behind a firewall, and others who beg people like me to please for the love of god stop giving away homework solutions because coming up with good algorithms homework problems is really really HARD.

Update:  Starting in 2017, I now regularly release solutions for my discussion problems, typically a few days after each discussion meeting.  (Just like homework solutions, I take these down at the end of every semester.)  Perhaps as a result, these discussion problems are now effectively fixed from one semester to the next -- in a typical semester I replace 5%-10% of them -- and discussion problems almost never appear on exams.  (I should also clarify that discussion problems do not contribute to the final grade.)

I also include an extra solved problem in each homework, with a complete grading rubric.  Again, these solved problems rarely change (as opposed to the problems the students need to solve, which change every semester).

In both cases, the idea is to provide concrete examples of the structure, precision/formality, and level of detail expected from their own work.    Realistically, once the discussion sections are over, unsolved discussion problems are not as valuable as the solutions; students are busy!  And I have lots of other unsolved exercises in my lecture notes for students who want unsullied practice.

Writing all those lab solutions (just over 100 pages of text) was a lot of work, but now it's done.  The net effect of releasing all these solutions seems to be positive—more clearly for teaching evaluations, but also for student performance.


MSc project in ML/Computer Vision - substantial achievement What would be a valid/substantial achievement in a Masters project (~30 pages, 4 months) in Computer Vision/Deep Learning?

Obviously it is not enough to just take an existing model (e.g. Mask R-CNN or similar), find/hack a dataset by yourself, add a layer or two out-of-the-box and finetune the model to it.

It is also obvious it can't be a huge contribution simple due to the size/nature of the project.

I'm thinking along the lines of implementing a new problem-specific loss function/accuracy metric, writing a new layer with custom functionality, and improvement of accuracy on a benchmark dataset.

What would be a valid/substantial achievement in a Masters project (~30 pages, 4 months) in Computer Vision/Deep Learning?

Whatever satisfies the people who actually certify the master's project in question.  In my department, that would be the student's advisor (and, as a formality, the department's director of graduate studies).  In other departments, that would be a committee.

Different departments and even different individual faculty, even in the same subsubfield, apply different standards for what is "valid" or "substantial", just as different publication venues (and different individual reviewers), even in the same subsubfield, apply different standards for what is "publishable".

In particular, departments necessarily set standards that are realistic for the actual population of students that they admit.  Baseline expectations at MIT may be wildly unrealistic at the University of Southern North Dakota at Hoople.

Different departments (and even different faculty) also have different philosophies about what the proper goal of a master's project should be.  Should it be a body of publishable research?  (Or more strictly, should it be a publication at a top-tier international conference like IMCL or NeurIPS, or at least a significant chunk thereof?)  Or is it sufficient to produce a useful piece of software, say for an industry client, or a well-written expository survey?  Or is it sufficient to attempt publishable research and settle for a useful implementation or survey?

  Obviously it is not enough to just take an existing model (e.g. Mask R-CNN or similar), find/hack a dataset by yourself, add a layer or two out-of-the-box and finetune the model to it.

No, that is not obvious.  In some departments, that would be considered a valid Master's project.  In my opinion, it wouldn't be a very good Master's project, but my opinion only matters for students in my department.


Are page references required when citing the same quote multiple times? In an MLA format humanities essay, if you've quoted a long chunk of text at the beginning, do you need to provide page references when citing phrases from it later on?

The guiding principle for citation is always clarity and unambiguity of communication. Thus, for example, if you have a direct reference nearby, no citation is needed:

  From the above quote, we see the phrase "like bananas for monkeys" is a clear reference to Shakespeare.

but if there is an intervening gap or other citations, then you should cite it as originally.  In the particular case for this question, where the entire essay is focused on a single fairly short piece of text, it seems that the reference is likely to continue to be quite clear, as long as the prose continues to make the link clear, as in my example, and thus no explicit repeated citations would likely be necessary.


A student who asked me to write a rec letter seems to have committed academic dishonesty in my class, what do I do? A student (let's call them A) in my class that I just finished teaching asked me to write them a recommendation letter. I said yes, and the first deadline is Dec 15, as they often are (FYI, I'm writing this post on the very early morning of Dec 14).
Unfortunately for the student, as I was grading the final exam, I noticed that A and another student (call them B) seem to have exactly the same wrong answers (their wrong answer was pretty unusual, and in my class of 100, they were the only ones to have these wrong answers).
I've actually been suspicious of B for a while, as B kept coming up with the correct answers without actually being able to justify their steps, and in fact, their work was often wrong up until the final answer.
Upon comparing A and B's exam papers, A's solutions were more or less clear, and I'm sure that A worked it out themselves. B, on the other hand, has solutions that are more or less like A's solutions, but there are so many critical typos and mistakes in notation (showing that B has no idea what is going on in the course) that it should be impossible for B to arrive at the correct answer. I think the errors were introduced because B was sneakily trying to change some letters and such to a different letter, and changing a sentence structure here and there, etc.
So, I suspect that A is actually a decent student, but for whatever reason, A let B copy their answers for the exams in the course (there were 3, all 3 exams were strikingly similar).
I'm planning to submit this case for an investigation, but this takes a while, possibly longer than a semester. My questions are:

Should I still write the recommendation letter? On one hand, if A turns out to be innocent (not possible in my heart, but innocent until proven guilty) then I'm screwing them over by declining to write a letter so close to the deadline. On the other hand, if A cheated, then since I was aware of this before writing this letter, I don't want to not mention it.
How would you confront the student?

You agreed to write this letter, and it is due tomorrow (!). Reneging on your promise would effectively torpedo the student's applications, as they are very unlikely to find another recommender on one day's notice. Given that you haven't even confronted them yet (much less convicted them), this would be altogether unjust. (Clearly, your dilemma would be very different if this incident had come to light a month ago.)
Given this timescale, I suggest the following:

Write the letter without any mention of your unproven suspicions.
Confront the student.
If the student confesses or is convicted, you could then consider contacting the programs to withdraw or amend your letter. This is a natural, just, and foreseeable consequence of A's decision to cheat in a class taught by their recommender.

One other thought: consider doing #2 ASAP -- you could ask the student if they are available for an urgent, important call regarding your letter of recommendation (that should get their attention). You can then confront them and also explain how this will (not) affect your letter. This conversation has a few possible outcomes:

You proceed as above. Indeed, I recommend writing the letter before having this conversation precisely so that your letter is not (even unconsciously) affected (as even innocent students may react poorly to serious accusations).
They withdraw their request for a letter. You certainly shouldn't pressure the student to go this route -- I wouldn't even bring it up. But it's possible the student will say that they prefer not to use your letter given the accusation; in this case, you should respect this.
They confess to cheating, at which point you can either rescind your offer to write the letter, or you can agree to write a letter that reflects this incident.

Note, if you do confront the student ASAP, you must be careful not to pre-empt the judicial process, nor to create an abusive, blackmail-like situation. Assure them that you have written a positive letter that does not mention your suspicions, and that you will not rescind or amend your letter until/unless they are formally convicted. Despite your best efforts, A will doubtless still be stressed; accusations of cheating are always stressful. But being transparent and giving A as much agency as possible is the best one can reasonably do under the circumstances.


Can I do experimental research after doing a computational PhD in Chemical Engineering? I am a graduate student in Chemical Engineering at a good research university in the USA. I came for the master's program and started working with a professor from day one. It has been a year, I like the research I am doing, and am on my way to publish a paper. The research is challenging and impactful but is purely computational.

I am moving to the PhD program here. There is no issue with research problems or stipend—I am fully funded—but the idea of pure computational for the coming  years is freaking me out. I am worried because I cannot run experiments anymore and I am wondering whether this limits my opportunities after my PhD. Am I going to narrow myself in research careers if I am going for a computational PhD?

Especially in chemical engineering, it is definitely possible for people to move back and forth between experimental and computational work, and even to combine the two in a single career (although this is much more difficult, obviously).

Both of my graduate co-advisors have made similar transitions during their careers—from a mainly computational focus to a primarily experimental focus. It is definitely feasible.

The main issue that you'd want to remember at the earlier stage of your career is that the further away you want to leap from your PhD research, the harder it is to find someone willing to sponsor you to do that. For instance, doing an experimental PhD related to the topic of your computational PhD should not be too difficult; moving from computational combustion to experimental biomedical engineering would be much more challenging.


Why are there no computer-friendly versions of papers? I believe that many people read articles from their computers. So, tiny fonts and many columns have no advantage.

Why don’t journals make alternative computer-friendly versions of the articles? For example, with a gray background, single column, double-spaced? I guess it wouldn’t be difficult because they are compiled with Latex, so, adding a single line of code would suffice.

I have heard about this problem from many students and professors. Aren’t journals aware of this demand? What can be done about it?

Why don’t journals make alternative computer-friendly versions of the articles?

They already do. It’s usually called HTML version. I couldn’t find any statistics on how widely available this is, but as far as I know, all major journals in my field (physics), all mega-journals, and Elsevier offer this.


Appropriately thank a supervisor for giving you a chance when you actually failed? In general:

What is the proper manner in which to thank a supervisor for allowing you to take a really long shot? I am talking about when a professor (supervisor) who did not really know you decides to give you a chance, even though there is clearly a very low probability of success. Especially in the case where the supervisor invested significant physical resources and/or their time and you still failed to accomplish the goal (in this case research result). I am looking for a formal and appropriate way to convey my gratitude to the professor for taking the risk. Again this is in the context where you have specifically failed to reach the goal and cost the supervisor in question a good deal of resources in the process. Is it even proper to convey gratitude at this juncture for giving you an outside shot at tremendous opportunity? Or should I just be extremely apologetic?

"Thanks for the opportunity" is almost always appropriate and welcome.

As far as "apologetic" goes, I'd think on why the goal wasn't reached. Some research projects are known to be long shots before they're begun and failure, while obviously not desired, is not an unforeseen outcome. Sometimes outside events beyond anyone's control torpedo things. If this is the "flavor" of your situation, then I don't necessarily think abject apologies are called for, though you can certainly commiserate with others involved on how things turned out. Sometimes research just doesn't pan out, and good research supervisors understand that you can't hit if you don't swing.

On the other hand, when I've been part of projects that failed in whole or part due to things I did or didn't do, I've tried to make sure the supervisor involved knows that at least I learned from the experience and that I had an idea of how I would do things differently in similar circumstances should they arise. Really, this is a valuable response in either case. Especially, though, if you hope to have future research opportunities with this supervisor, I'd make sure this is understood.


How could a postdoc look competitive against a tenured professor? I am a postdoc in mathematics (US) in the process of applying for tenure-track positions. The job market was very competitive and I only ended up with one interview so far. But I found out that for the position that I am being interviewed for, there are several other interviewees, and one of them is a tenured professor.

To me, the competition seems unfair because obviously the university wants the best candidate, and since the tenured professor received her PhD 10+ years before me, she has a lot more publications than me. And there is only one position for the university.

The situation looks hopeless to me. Is there anything that I can do or say during or before the interview that will make the hiring committee take me seriously? I have of course read several posts on here before asking this question. The answers seem to say that the "promise" is also taken into account. But will it actually be taken into account when the tenured professor has fifteen more papers than me? Since she also has more experience speaking in conferences and knows what to say or do during interviews (since presumably she has also served on hiring committees), I think her job talk will be better than mine, too. I just don't see how I could even be called a serious candidate when she is interviewing for this position.

You are a serious candidate

You are. That is why the university is interviewing you. It would be a complete waste of time and resources otherwise. They liked your application materials and want to know more.

Candidates must have good potential

Generally, academic hires are made not on the basis of what you have done but on what you have the potential to do. This is why the job doesn't automatically go to the person with the most publications.

Search committee members know how academia works

Of course the tenured professor has more publications than you! Of course they have more teaching and advising experience, and of course they have more contacts and a wider network within the field. The search committee knows this, and they don't expect postdocs to have all of these things. But perhaps the postdoc looks like they have the potential to blossom into a better scholar than the tenured professor. Perhaps the tenured professor will just stagnate. Which brings me to my final point...

You have seriously incomplete information

You have basically no knowledge which could help you figure out your odds. Here are some possible scenarios:

The tenured professor (who I'll call P for brevity) is not actually serious about this job, but just wants an offer so that she can have a counteroffer from her current institution to get a nice raise.
P is fleeing from a toxic department; she is on bad terms with her current department chair.
P is leaving her current department because she is effectively being pushed out due to a scandal involving an affair with a student and misappropriation of research funds. This is hot gossip among the more senior members of your field but no one knows the full details.
P is applying for the position to live close to her ageing parents. She thinks that she will easily get the job because she is from a prestigious university and this job is at a mid tier school, however her arrogance poisons the interview and no one likes her.
P is the best candidate on paper, but just doesn't "click" week with the current faculty, so they vote to hire someone else.

We can go on like this for all the candidates, including you. Then we can invent scenarios of what the search committee is looking for, pet peeves of individual members, and so on. But the fact is we just don't know, and even if we did know then there's nothing you can do about it.

Get back to work

Quit worrying. Use that nervous energy to polish your talk, to practice answering questions, and to do research about the school. The job goes to the best candidate, and that could be you.


How to protect your unpublished ideas? My advisor introduced a new student in my work saying that I am doing X,Y,Z and he will be doing A,B,C. I was asked to explain X and share all the data, results, reports, all related publications etc   to enable the student to do A,B,C. Once I have explained him X, Y, and shared my data and future plans, he changed/abandoned his directions and submits a paper with another lab collaborator and my advisor on my key idea X without even letting me know anything. After somehow finding it, I politely asked them about it. They were apologetic and both these students agreed through email exchanges that it was not their work and that they should have credited me. They also agreed that this should not have happened after a little confrontation.

I know, my advisor is fully aware of the whole situation, and this was done deliberately. I am not able get out of this whole situation for a long time.  Taking a fight with my advisor or the students is not an option. How would you all handle this situation effectively? How can you protect your ideas that are unpublished but you have been asked /instructed to share with others? Even I have experienced situation where the supervisor himself misuse and steals student work without giving credit to the student who did it. So a tough question is, how do you protect your ideas from being stolen by your advisor?

Obviously your idea is not 'protected' as someone else might have the same idea and/or a subset of that, and make a publication/patent out of it before you do; and you can't claim that the person stole it through your brain waves!

Write down the introduction and background to that idea you have, and prove that it is your contribution through case studies and evaluations, and submit it to be published in a conference as a poster or a paper.


How to quantify the loss in productivity due to time spent on writing proposals During my PhD it bothered me how much time my supervisor had to spend on writing proposals to get funding to do science, which in practice pretty much meant that he had no time to do science because he spent all that time in the time-consuming business of getting money.

As I was finishing my PhD and looked at postdoc opportunities, I wrote myself a project proposal for starting investigators in which I spent overall about one month (literature review, securing collaborators, writing itself, etc). This was very competitive and only one in every 100 applicants got funded - I did not get funding, although I made it to the interviews, which I was told meant I made it to the top 10%. I am not completely unsatisfied about the outcome because I gained experience and contacts, which eventually led me to being offered a postdoc position by one of the people I had included as collaborators. However, the whole process of putting the thing together meant I lost about one month that I could have spent doing science. It also made me realize that even writing a high quality proposal in which I had spent a lot of time working on the details would not necessarily lead to guaranteed success. If only 1% of applicants get funded it means that statistical noise alone is enough to push you out of the winners pool!

Now that I am a postdoc I have to spend some time helping my new boss with his proposals and in the near future (maybe in the next year) I will have to start applying to some competitive project money myself. Again, this means that I will not be doing research during that time and will spend a considerable amount of time trying to get that research funded.

How can this loss of productivity be quantified? Are there studies on how much less research is carried out because of the time spent on competitive hard-to-win grant calls?

I think the question is barking up the wrong tree, to mix metaphors.

As you proceed through the researcher lifecycle (Ph.D. student -> postdoc -> professor), your responsibilities will change. For instance, one popular criterion for paper authorship in psychology is that one should have contributed to two out of the following four aspects of research:

Grant acquisition
Data acquisition
Data analysis
Manuscript preparation

As a Ph.D. student, you will mainly work on 2-4. The postdoc will spend less time in the lab (point 2), more on 3-4... and he will also start working on grants (point 1). Finally, the professor will mainly focus on points 1 and 4, to a degree on 3. However, the bottom line is that all four activities are "doing research", since grants are just the way money is allocated to competing research groups nowadays. (Of course, one could argue that it would be better if funding were just distributed equally among researchers, but that seems to be a different question.)

Similarly, in industry you could wonder how much time, money and energy is spent on marketing, pre-sales and sales activities and how all these resources could be much better spent on R&D, production and actually serving customers. But this would miss the point that without salespeople, there would not be any customers, nor money to spend on all the things the non-salespeople like to do. (And frequently, the conversion rate on business proposals is similar to the 1% you quote.)

So: don't see writing grants as a drain on productivity. Writing (and getting) grants is how you get the money to do everything else in science.


Exchanging co-authorship for proofreading? Is it an ethical practice to offer co-authorship on a paper to a graduate student in exchange for proofreading the paper? By proofreading, I mean fixing small grammatical or spelling errors right before manuscript submission, not contributing significant comments to the experimental design or methodology that are later taken into account.

I ask because in my old lab we had several international students and several fluent English speaking grad students, and the fluent English speakers are listed as co-authors on several papers where they have contributed nothing more than small grammar corrections.

I think this is professionally unethical. Other fields might have different views, but per the International Committee of Medical Journal Editors, authorship is based on

  Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
  Drafting the work or revising it critically for important intellectual content; AND
  Final approval of the version to be published; AND
  Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Since this graduate student has no intellectual stake in the work, this is basically lying. The fact that the student would benefit from a publication (and you, if you are their adviser) makes the conflict of interest all the more apparent.

The appropriate place for such a contribution is in the acknowledgements or not at all.


How should I tell my advisor that I want to leave my undergraduate research position? I am about to wrap up my first semester at my University. Before school had started in the summer I had developed an iOS application for the University which gained the attention of one professor, before the semester had even started he emailed me and informed me of a research position he think I'd be interested in. It turns out that I have experience in exactly what they are doing and it sounded like it would be a great fit.

I accepted the position, it took about a month before I met who I would be working with and how exactly I would be applying my experience. I found out that my colleague developer is a foreign graduate student who doesn't speak much English and has hardly any experience with our technologies. We setup weekly meetings to do something, nothing is ever accomplished at these "meetings". Most of the time the professor cancels the meeting due to "conflicts" an hour before they are supposed to take place

Overall I've got several problems with my current position:

No way to collaborate with my colleague due to a language barrier and no structure in our work environment

No clear objectives for what I should be doing with my time have been laid out, yes I've asked. I get responses back such as "Do some iOS stuff with "Bob"" Whenever I sit down to work, I really don't have an idea on what he's expecting so its hard to put in any meaningful hours.

Extremely low pay for what I am doing, the technologies I am working with are not simple and I am getting paid about 1/5 what I would if I was at a company. I know this is an academic position but it's hard to be motivated knowing I'm getting low pay.

The project I am working on seems to be some project that has been hanging around and he is just trying to produce "results" with the grant money.

Overall I just have no interest continuing to work with this professor. What is the best way to tell that to him?

Just tell him the way you have told us.  Undergraduates quit research projects all the time.  In this case the reasons you have given may help him run the project a bit better.


If I don't participate in research in first couple of years of PhD , how much am I losing? I completed my MS in Human Computer Interaction (HCI) and was planning to do an PhD in Operations Research. But after first few weeks in program , I decided it was not what I wanted to do in long term and dropped out.

There is a background to this- my untenured MS advisor refused to sponsor my PhD . Another well known professor in the department came forward and offered me a good stipend but I dropped out from his lab after couple of weeks.

Things had become too caustic even before I joined his lab. The PhD professor wanted me to help him with some algorithms in the final month of my MS, but my MS advisor shooed him multiple times saying that I was very busy, when in fact I was free all the time. My MS advisor wanted me to make her a co advisor and get her a piece of the tenured professor's project, it didn't work out and since then she has become pretty vengeful towards me. She rejected job offers from our research sponsors after MS saying I was doing PhD and was not interested in working for them.

I think at this point I have lot of ill will against me in my department mainly due to active efforts to undermine me by my MS advisor.

In my university ,we need to take around 30-40 credit hours of subjects and pass candidacy and continue the dissertation.

I am taking an completely different route to PhD. I am taking 9 credit hours every semester (full time course load is 6 credit hours) and at same time working full time as Data warehouse SW developer.

It's very strenuous(My long commute, job and courses and assignments occupy at least 17-18 hours of my day) but I am able to manage them and it's working pretty well as I get paid around 135K at my job.

Is this is an ideal approach until I finish my subjects and then see if any HCI lab in my department can take me in? Can I expect things to calm down in 2 years time.or what can i do right now to improve on this situation?

Or am I really hurting myself here. My concern at this point is after my candidacy, no lab will take me back because of no recent research work. By the way, I don't need to finalize an advisor for the dissertation right now, I can take candidacy exam in dept. by forming a temporary committee .

One advantage I may have is I will have enough money by that time to volunteer in a lab for around an year to prove my worth. But again I am afraid my MS advisor may actively sabotage any of those efforts. I have been afraid and tensed for past few days thinking about this.

Edit : I am from Midwest US

I think it's better to be involved in research from the beginning of a PhD program, but I've seen plenty of programs designed to discourage starting research until the student has passed the qualifying exams. Both models can lead to good PhDs and good careers afterward. You appear to be in the enviable position, assuming you can sustain it, of being able to save a substantial portion of the salary from your day job so that when it comes time to start some research, you can do so with your own funding. If the project goes well, and you are good at research, you may be able to complete your dissertation more quickly than someone who has to work on a funded grant which has deliverables other than your thesis.


Is it acceptable for a referee to reject a paper for superficial reasons? I recently got a short referee’s report on a paper I submitted.
They made superficial remarks concerning notation and writing style, and then admitted that they did not even read the main result.
Is this acceptable for a referee?

The editor took their recommendation and rejected the paper.

Referees don't reject papers: editors do.  You may feel the report was superficial but apparently the editor does not subscribe to this point of view.

You can make an appeal to the journal or the editor, pointing out politely that since the referee (alledgedly) admits to not having read the body of the paper, you would be grateful if the editor could add some constructive comments so you can make changes and resubmit, but don't hold your hopes up.

You are unlikely to win by challenging the report directly: if your appeal looks like a complaint it will be ignored; you are more likely to get some traction if you ask for clarifications on why the report was so short, why referee did not bother to read the main body, what specific published work that the referee used to reach her/his conclusion, etc.


Large distributed research network I remember having heard of a large distributed computer network being operated by several universities. As far as I remember, it would be possible to get several (hundreds) of virtual machines to perform computations, testing, evaluations, etc.

Unfortunately, I do not remember this network's name, but I have the feeling that it was similar to "ResearchLab" or "ScienceLab". However, I was not able to find a network with such a name...

Hope that anyone out there can help. Thanks a lot!

You're probably thinking of PlanetLab.

  PlanetLab is a global research network that supports the development of new network services. Since the beginning of 2003, more than 1,000 researchers at top academic institutions and industrial research labs have used PlanetLab to develop new technologies for distributed storage, network mapping, peer-to-peer systems, distributed hash tables, and query processing.

  PlanetLab currently consists of 1335 nodes at 645 sites.

More generally, there are lots of "testbeds" (as these networks of distributed resources are called), including Emulab, PlanetLab, ORBIT, DETER, Seattle, and many others. Each has various advantages and disadvantages, depending on what you're trying to do. These are often federated so that users can access resources from multiple testbeds; projects like GENI and FIRE are federations of testbeds.


Who should approve changes in curriculum? In the US and UK higher education system, who is responsible for a program curriculum?

For example, if planning to modify the curriculum by adding an uncommon course, who should approve this modification?

Consider that the change is truly uncommon. Can the department head decide about it? or needs the approval of higher officials (such as dean or VP for Academics) or there are boards and committees to decide uncommon changes (for the sake of education quality assurance).

In the US, this is a classic example of shared governance. Typically there will be a well-defined bureaucratic process for proposing a new course. You write an outline and fill out a bunch of forms. Then the other faculty in your department review it. There will also be layers of approval by managers, but the managers are supposed to be reviewing the course for administrative stuff (e.g., maximum section size, which has fiscal implications), not for content (which is where faculty are the experts).

It's important, especially at a community college, to make a course that will be useful to students if they transfer to another school. E.g., I recently proposed a course, Relativity for Poets, at the community college where I teach, and we wanted to make sure that it would transfer to the state university systems so as to satisfy gen. ed. requirements. At community colleges, these concerns may make it essentially impossible to offer a new and innovative course if there is nothing like it in the state university system.

Assuming that a new course does go through all the approvals, it's still not guaranteed that the school will offer it. For example, they could decide that it's too expensive to offer, or there might not be enough demand.


Quality of English in a college recommendation letter Does the quality of English in a letter of recommendation matter? Should it be really good or just average English with occasional mistakes suffice as long as the meaning is clear? Is a perfectly written letter with no grammatical mistakes better then a letter with few mistakes without flowery language if the points mentioned in both are the same?

The people reading a recommendation letter are interested in the following two things:

What is the assessment that the recommender makes of your skills and potential?
How much should they trust the recommender's assessment?

The style and errors of the recommender primarily speak to the second point.  It is hard to see how an occasional "normal" mistake will make a significant difference in how your or the letter-writer are perceived.  Likewise, if the writer is not a native English speaker, but makes mild typical grammatical errors that do not impede the sense of the document, it is just part of the normal "friction" of cross-cultural scientific communication and should not be a problem.

If, however, the mistakes are frequent and significant, then it may undermine the credibility of the recommendation.  Frequent or severe mistakes can make a reader think either that the recommender doesn't really care about this letter or that they are sloppy and perhaps don't have very good judgement to begin with.  Fairly or not, this can also apply to foreign-language issues, since a professor who knows their English is not good can get proof-reading help from any number of sources.


Why is getting your PhD in Iran invalid for obtaining a post-doc position in the US? My friend earned her PhD in Tehran Iran, I assume from the primary university. She received many awards and published several articles in Condensed-Matter/Cosmology. However when she applied to several post-doc positions in the United States, she was denied, not because of her experience, but because her PhD was earned in Iran. I do not understand why this would cause a problem in the United States. She was forced into a situation to either chose to be admitted as a graduate student and earn her PhD again, or simply stay in Iran. She is currently admitted as a graduate student in the US.

Why is getting your PhD in Iran invalid for obtaining a post-doc position in the US?
Is there away around this problem? Can she bounce off from her graduate position?

I think the answer is simple: your premise is false. There are no rules/regulations forbidding an Iranian earned PhD from being a postdoc in the US.

It might be hard to get a postdoc in the US for anyone whose academic background, and PhD earning institution, is not perceived as highly ranked.

Note: I have answered this a year and a half ago. Things may have changed since then through the presidential ban against Iranians entering the US. I have no specific knowledge of the current situation with respect to Iranian students.


Information with no reference in literature review I am writing a literature review and I've found information in a thesis written in the literature review in a paragraph with no source, just ending with full stop. While at the end of the whole paragraph and after three full stops there is a cited source. How to cite the information with no source? Does that mean it belongs to the author who wrote that thesis or the whole paragraph belong to the source at the end? I am a little lost..

If you're confused about whether information comes from a citation, the clear solution is to go to the source being cited and look for it there.

You will then find that one of two cases applies:

Yes, the information was in the source, and the citation was just confusing. Now you've read the source yourself and can cite it in a less confusing manner.
No, the information was not in the source, and you have no idea where it came from. Then the author either made a mistake or was dishonest. At this point, you either need to disregard the information as unreliable and unsourced or else try to contact the author to get a source.


Can an applicant's funding offer for PhD programs in the US be taken back and given to someone who's already declined it? This question pertains to math graduate programs in the US. Suppose a school sent a financial offer (TAship) to a student A and put a student B on the waitlist in the event that A declines the offer. Student A then declined the offer by signing the letter, so the school sent the offer to B.

My question: is it possible that the school will rescind the offer from B if A changed his/her mind later on and decided the take the offer given that

i) B has not yet accepted the offer.

ii) B has already accepted the offer.

iii) The date that A signed the letter to decline was before April 15th.

Note that your question is not really math-specific, but applies to essentially all programs at schools that have adopted the so-called April 15th resolution of the Council of Graduate Schools.

Once a student has turned down an offer of financial assistance and the offer has been given to someone else, it would be exceedingly poor form for a department to "claw back" the offer to give it to someone who has already declined the offer. If the student really wanted to attend—and the department still wanted the student—it could perhaps use a declined offer to get the student, but it couldn't take one that was "in the field" or already accepted to do so.

In general, the student has one chance to accept or decline. Student A could have waited until April 15 to decline the offer but decided to do so early. That decision is essentially irrevocable. It is also impossible for the school to rescind the offer to B once B has accepted, unless there exists cause to do so. (Examples where cause to withdraw a financial offer exists might include if student B fails to graduate or is shown to have made a fradulent application.)


How to phrase authors' contributions when the convention is that all team members are authors? The question
When a journal (such as one of the Nature family) asks for information about authors' contributions, what is the most effective way to answer this, to ensure that the co-authorship is assigned as intended, and to ensure that reality is represented: that the work is a product of the intellectual environment of the whole team; and that all members have contributed in various degrees to the analytical methods used, to the research concept, and to the experiment design. Is what I've just written there sufficient, or is there a more judicious phrasing that is accepted by heavyweight journal publishers such as the Nature Group?
The context
The primary context is publications from a team that works on the principle that all members of the team are to be named authors on all papers coming out of the team.
I'm very specifically looking for answers from editors who handle such papers; or from members of teams with similar rules. Answers from others are of course possible, but I'd value direct personal experience.
Here's the Vancouver Group's (International Committee of Medical Journal Editors) statement on the subject:

Authorship credit should be based on 1) substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; 2) drafting the article or revising it critically for important intellectual content; and 3) final approval of the version to be published. Authors should meet conditions 1, 2, and 3.
When a large, multicenter group has conducted the work, the group should identify the individuals who accept direct responsibility for the manuscript. These individuals should fully meet the criteria for authorship/contributorship defined above, and editors will ask these individuals to complete journal-specific author and conflict-of-interest disclosure forms. When submitting a manuscript authored by a group, the corresponding author should clearly indicate the preferred citation and identify all individual authors as well as the group name. Journals generally list other members of the group in the Acknowledgments. The NLM indexes the group name and the names of individuals the group has identified as being directly responsible for the manuscript; it also lists the names of collaborators if they are listed in Acknowledgments.
Acquisition of funding, collection of data, or general supervision of the research group alone does not constitute authorship.
All persons designated as authors should qualify for authorship, and all those who qualify should be listed.
Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content.

The caveats:
 1. My field is not medicine: I've used the Vancouver Group's guidelines, as I haven't yet found any in my field.
 2. note that this is emphatically not about the ethics, pros and cons of such a rule. If you want to discuss that, please do so in chat. I may join you there as time allows
Related literature:

Author contributions audit, by Maxine Clarke on the Nature Nautilus authors' blog, lists example contributors' satements
Drummond Rennie discusses "Authorship Credits"
Academia and Clinic, by Yank & Rennie: Disclosure of Researcher Contributions: A Study of Original Research Articles in The Lancet
and lots more at the US Council of Science Editors

If there is a requirement to list the contribution of each author, it seems the ethical way to handle it is, well, to list the contribution of each author.  If some authors did not contribute directly to the paper, you should state that.

It is unethical to try to conceal that some authors did not make a direct contribution.  Please note: Your team policy is what it is, and I'm not trying to judge the ethics of your team's policy.  I'm just saying that, whatever your team's policy is,  you must disclose it forthrightly, without evasion.  That's exactly the point of disclosure requirements!

(If you're not comfortable stating your team's policy explicitly, that might be a hint that it's time to consider changing the policy -- but until then, you must disclose it frankly and honestly.)


Funding in mathematics from large companies as undergraduate I am currently a second year mathematics undergraduate and will be undertaking a research placement (in topology/decision problems in group theory) at a maths faculty in England this coming summer.

My supervisor has asked that I apply for research grants. Currently I have the potential to be funded (at least partially) by the maths faculty, and also the London Mathematical Society (LMS). To be safe though I would like to apply to as many places as possible.

I have heard of large company funding research in mathematics so perhaps I should just email large companies and ask them if they are willing to help fund my project? Has anyone heard of success of this before?

Are there any other kinds of funding sources I have not mentioned above?

Charitable funding from large companies seems unlikely
In the US, only about 4% of scholarship dollars come from private sources.

Here is the breakdown of sources of college grants: federal government (44 percent), colleges (36 percent), state governments (9 percent), employers (7 percent), and private scholarships (4 percent).
-"7 Things You Need to Know About Private Scholarships", U.S. News (2011-02-01)

So, you're in the UK and asking about summer research funding, but it seems likely that the same trend would apply: that not much of the funding would come from corporations.
The big problem's that there doesn't seem to be a reason why they'd do it.  Even if a company wants to give its money away to research, it seems like they'd target medical research or something else with near-term applications.
You may want to think out an argument for your research
In general, the problem's that capital's a finite resource; people could just as easily put their economic power toward medical research or their own interests.  So, why should anyone want to give you money?
This isn't rhetorical or to make you feel bad!  Rather, it's an important life question that we all have to ask.  And if you can find a good answer to it - one that isn't biased by your own personal wants, but rather is objectively compelling - then that'll give you a basis for pursuing sponsors.


Professor and Class Laughed When Asking About Overflow In Project. Did I do something wrong? I'm looking to stop lurking the stack exchange community and actually participate a little more.

A little background, I am a student in University in my freshman year of my Computer Information Systems - Programming Track Degree.

So this issue I have occurred about a week ago in my freshman visual basic class. Now I have been programming on/off since I was 11, so I knew quite a bit of the basics already. I understand that I am a little farther ahead than some of the students, but I don't ever talk about it or show off. I am very reserved about that kind of stuff.

Actually, this instance was the first time I openly talked in the class and asked a question. We were assigned a lab to add up every integer between 1 and the number entered by the user, and then display the sum. So if the user entered 10, it would display 5050. No big deal, so I get started.

Now this is where the issue happened. I realized that the user could enter a large number and cause an overflow error if we just used Int32s, which was all he went over in the class. I wanna figure out if he wants us to use Longs so we can calculate those higher numbers, or if he just wants to use Ints and catch the overflow.

So here I am, hand raised, about to ask the first question I have ever asked in this class. I said something along the lines of, "Would you prefer us to use Longs or to use Int32s and just catch for overflow."

I kid you not the response I got from the class literally made me slide down in my chair. The Professor starts laughing and says "Use Int32s without checking for overflow." And the whole class around me starting giving me weird looks and some even vocalized, "Why would you do that?", "That's stupid", etc.

In my head, my question made complete sense. I mean, I'm not going to claim to be a programming god, but I thought that by now I had basic stuff like checking for errors understood. Why would not encourage us to do that / am I just being stupid?

All I know is that I was extremely embarrassed.

PS : He did already go over Try Catch in the class, and the Long data type. So the class would have the knowledge to check as long as they knew what an Overflow error specifically was.

TL;DR - I asked if we should check for overflow in a program in my class, Professor and whole class acted like I was crazy. I feel embarrassed.  Did I do something wrong?

Assuming that there are no missing information on the situation:

Your question does make perfect sense. Overflows are a serious flaw that can lead to security problems, not a laughing matter.

However, I can see myself leading the students to an overflow as motivation to explain how to deal with them, that might include answering your question with the same words your professor used (to not disrupt the 'plan' for the topic - Indeed, I did that a couple of times already).

This would not, however, include me laughing (a smirk maybe), and I would certainly not tolerate that reaction from your colleagues, which would lead me to reveal what I was planning and that your question was not only valid, but the point of the whole thing.

Ignore the reaction of the class. Herd mentality... As if they knew any better :)

On a side note, who teaches visual basic nowadays? :)


What makes a transcript official vs. unofficial? What really determines whether a transcript is official or unofficial? (I am wondering mainly in the context of US high schools and colleges.)

Is it entirely about the process by which it is delivered, i.e.
directly from an institution to a the receiver such that there can be
no tampering?
If an issuing institution (such as a high school) does not print the
transcript on special security paper, but sends directly to the
receiver in the mail on regular paper, would this still be considered
official?
Are there cases where an official institution would consider a
transcript to be official but an receiving institution would not consider it to be official?

An official transcript is usually sent under some sort of seal, with measures to ensure that the document has not been altered. When delivered to a student, it will usually be in a sealed envelope in such a manner as it will show proof of tampering or opening.

The usual method, though, is for the issuing school to send it directly to the intended recipient. The transcript is then authorized as "official" through some combination of stamping or seal. (Nowadays much of this can be and is handled electronically.)

There may be some schools that have specific rules that would prevent them from accepting what another school sends, but you'd have to look at individual schools' policies to know for sure.


Improve my relationship with this professor? When applying to graduate school, I didn't asked for a letter of recommendation from a professor who offered one.

Now I have a course with him and he seems to avoid me. How I can fix my relationship with him?

What exactly "avoid" means in your situation? Is there any specific event or occasion?

To me, building a relationship with someone is hard especially when It comes to professional life (e.g. academia). It takes lots of effort and time. Did you spend time with him like asking questions in his class, talk to him, or even disagree (debate) with him on a certain topic? I don't talk about the relationship outside of the classroom.

I think this is just your bias about what that professor think or feel about you and you care too much about that. Most of the time people don't care much about other people unless there is common voice or opposition about something between them.

So, if you want to have a better relationship with that professor, my suggestions could be something like (based on my experience):

Actively discuss when you're in his class.
Email him asking about some of your ideas.
Buy him a cup of coffee :)

I'm not sure if this helps but hope you feel better.


Is starting with the abstract and conclusion an efficient reading approach for writing a literature review? I'm writing a literature review at the moment. I have many many papers to read and I don't have that much time (just one month).

What I do is reading the abstract and then the conclusion and from the conclusion I see what were the key findings and then look them up in the paper and see some details.

Is that good way or should I read them cover to cover?

Your strategy sounds sound to me. You cannot read the papers from end to end. You need to weed out they papers you will possibly need in your review with as little effort as possible. Title and abstract should be enough. Once you have done this you should be left with a bunch of papers that are likely to be of use to you. While you have looked through them you must also consider how the material can be organized. There are many ways of doing this, chronological order (boring), thematic order (are there subfields?), contradictions (are there compeeting ideas) etc. The point is to try to get the information organized. You need some form of organization fo the papers so that you know how you will treat them in your review.

Once you have found all papers you think you need you can start assembling the information into your text. It is at this point you can start reading them in more detail. It is still not necessary to read every detail. you need to focus on the background to the different conclusions drawn.

The goal of the review should be to provide a new perspective on the field you have chosen so your own familiarity with the field (papers) is key. Try to collect as many as you can; not all will be used.


I failed a written exam and I don't know how to reply to my professor As a preface, I would like to explain that  have ADHD and anxiety issues and tend to be very slow at writing (mostly due to overthinking/over-analyzing and having OCD-like tendencies when writing).  I also made the dumb decision of not going into academic services to ask for accommodations (partly due to anxiety).

Due to the stay at home order the exam for my class was changed to have only written response questions (thankfully the professor changed it to open book). We had the whole day to work on it but then it was time to turn it in, I had only a third of the questions answered.  Yesterday  I received an email asking about why the exam was sent incomplete  and if I sent the wrong one. I had a minor anxiety attack and made a  unwise decision of avoidance. Today, I am trying to write a reply to my professor's inquiry and have no idea how to explain it. I'm worried that my professor will think that I don't care about the class and that I'm slacking off.

Tell the professor what happened.  If the professor already knows about your issues, there's no need to belabor them further.  If not, summarize as you did in your question here.

Explain that it was incomplete because, although you worked for the entire allotted time (if you did), you didn't finish.

Your question suggests that you may be used to an oral component on exams.  If that's the case, perhaps the professor would examine you by telephone, or Skype, or something.

The professor's email, as summarized by you, shows that the professor cares about you and wants you to do well.

Addendum: If you have academic trouble, always start with the professor.  If you get an answer you don't like, you can then move on to department heads, academic or disability services, etc.  Please give the professor a chance first.


How to make sure one understands each and every detail while reading a textbook/research paper without leaving any gaps? I am a physics Master's student.  I have good grades, with CGPA above 9(/10)(However, all courses in my university are graded on a curve so the actual marks are significantly lower than 90%). This has been my marks throughout my undergraduate and graduate courses and I was happy because this was above average. Also, my summer projects in UG were usually computational and any difficulty I had in theoretical aspects I attributed it to my inexperience in advanced Physics.

However, recently I have started to work on pure theoretical project and while reading for the project I realized that I consistently miss some details from each topic. I understand the general idea, can re-derive most of the equations(with some effort) however when I discuss things with my professor or when I read further topics I realize that I have missed certain key-details. This is exactly similar to the case of me missing a few key questions in my exams and getting that 9 grade point instead of 10(or at-least I think so). I was not worried about this before but now I realize that there is some flaw in the way I study (but I do not know what).

Since I want to apply for Physics PhD I realized that I will need to have solid concepts and it would be best to correct my mistakes but I do not know how or even what could be the reason for my imperfect understanding.

Any direct suggestion would help or even certain general guidelines on what I can do.

Things I've tried recently -

1) I've started to written down almost everything I read. This is slightly laborious but is certainly helping. It also helps me to gloss over the things I read previously when I come back the next day.

2) I completely avoid smartphone when I'm library/study room so that I won't get distracted. Not sure if it helps me solve the above problem but certainly helps me study better in general.

3) I've tried to read the topics from several sources. The books I am reading is somewhat like a collection of research papers and hence does not have a exercises. So I though reading from various sources might help get better ideas but it's not really helping because most other sources are too high level as an introductory text. But this will probably help me in cases where it's practical to do this.

4) Somewhat similar to point (1)- I am trying reread, whenever I can, what I've done until that point. But not sure how much this will help me in filling the gaps.

I will give a separate answer for textbooks and for scholarly papers. The difference is that textbooks normally come with exercises to help solidify the understanding of the material.

For a scholarly work in a technical field, however, a proven method is to read the paper three times, but with a different focus each time.

The first reading is something like a deep skim. You are looking for an overall, not a detailed, understanding at that point. Take notes as you go, but don't just try to copy things. The notes should capture the main points of the paper. What was the methodology, what are the main conclusions. But also take notes about what you don't get from this reading.

Wait bit, a day or so, before the next reading so that the ideas have a chance to settle in your mind.

The second reading is deep. Here you are trying to follow the arguments of the paper in detail. Use the notes from the first reading to guide you. Think about how the paper extends the field. Think about what it does that is new and interesting. Take notes also. The notes include those things you don't yet understand. Different colored note cards are very useful for different kinds of notes.

The third reading, again after a short wait, is to solidify the second, if needed, but also to look for ways that the paper can be extended. What future research does the paper suggest, explicitly or implicitly. Again, refer to your older notes and take new ones.

Then, after the third reading, summarize your notes. What is most interesting about the paper? What is incorrect in it? What are the important things to retain?

Note that not every paper requires this deep understanding, but those that relate to your own research probably do.

For textbooks, you can do the above, but it may not be necessary for two reasons. First, a scholarly paper is directed at a small group of specialists and you may not yet have the skill and experience to understand it quickly, but the textbook is supposed to be written for people still learning. So, it should be more accessible, using more complete arguments, say. But you can also, intersperse your readings with the exercises. Ideally you should be able to do every exercise in the book. (There are exceptions, in which research problems are hidden in the "exercises", but these are rare.) If you don't have an obvious solution to an exercise, seek feedback on your attempt. Even if you don't have time to do every exercise, you should read them. Think about how you would attack that exercise.

Time is obviously an issue if you adopt/adapt this method. One way to handle that is to make sure that you don't waste time, not that you spend longer hours. If you take notes on notecards and paper then you aren't tied to your desk. If you have periods of inactivity, say on a long commute by bus, make sure that you always have a copy of the current paper and a few note cards. Even if there are only a few moments, waiting in a line to buy coffee, you can review your note cards and jot down a few ideas if they come.

There is a learning theory behind all this. Deep learning requires reinforcement. You don't "learn" something by reading or seeing it once. Feedback from exercises is also an important element, but harder to get for papers. One way to do that is to read a paper as part of a study group and have a discussion after, say, the second reading. Discussions with the professor for students can also work if it is available.


How do I determine which of two International Conference on Web Services (ICWS 2018) is genuine? International Conference on Web Services (ICWS) is one of the top conferences for web service researchers. But this year (2018), I find there are two ICWS websites:

http://icws.org/2018 (this one is the old website for ICWS 2017, 2016..)
http://conferences.computer.org/icws/2018/ (this one is sponsored by IEEE)

How can I determine which conference is genuine? And as I'm a Chinese researcher, another thing I care about is that which one is recognized as the B-level conference by CCF (China Computer Federation)?

Looking a little closer, the IEEE conference in San Francisco is the one that has prior proceedings, and it has an "Important Notice":

  Note that any other similarly named conferences are fresh starters beginning this year and operated by a different organization in Seattle, Washington that has nothing to do with IEEE.

The thing I can't immediately tell is whether the Seattle conference put on by "Services Society" is real or fake.

That conference says in relation to its proceedings:

  The Proceedings of ICWS 2018 will be published in the Lecture Notes in Computer Science (LNCS) by Springer International Publishing AG. Currently the Abstracting and Indexing services covered by Springer’s data feeds for LNCS proceedings include ISI Conference Proceedings Citation Index (included in Web of Science), Engineering Index EI (Compendex and Inspec databases), DBLP, Google Scholar, IO-Port, MathSciNet, Scopus, and ZBlMath.

I did confirm that the Seattle conference is on the list of upcoming Springer Proceedings. Springer published several prior proceedings related to ECOWS 2004 and 2006, the conference the Services Society conference traces its history to:

  the International Conference on Web Services (ICWS) was first conceived in June 2003 in Las Vegas, USA. Meanwhile, the First International Conference on Web Services - Europe 2003 (ICWS-Europe'03) was held in Germany in Oct, 2003. ICWS-Europe'03 is an extended event of the 2003 International Conference on Web Services (ICWS 2003) in Europe. In 2004, ICWS-Europe was changed to the European Conference on Web Services (ECOWS), which was held at Erfurt, Germany.

The IEEE conference says it goes back 12 years.

The Services Society has been a registered non-profit organization in the U.S. since at least 2011, according to this IRS lookup. The Principal Officer on that registration is Zhixiong Chen of Mercy College (the mailing address on the registration matches his office address).

He mentions on that same profile page:

  President of the Services Society, a non-for-profit organization, promoting collaboration between Academia and Industrial practitioners on Services Computing and delivering relevant open course modules, sponsoring with IEEE conferences like IEEE Services 2010

So, my best guess is that Services Society is related to some entity that had been involved in running the initial European Conference on Web Services; that conference perhaps became affiliated with IEEE and the name ICWS; Services Society had some minor affiliation with the IEEE ICWS; and now it is running a separate conference of the same name. (In fact, according to an announcement in IEEE Xplore, the Services Society ran the 2009 School on Cloud Computing with the 2009 5th IEEE Congress on Services (SERVICES 2009).)

What I do not understand is why, if that's the case, IEEE would say there is absolutely no link between the two. The poorly sourced ICWS Wikipedia page claims that ICWS is run by both Services Society and IEEE.

It is possible that the Services Society conference in Seattle will have a legitimate conference, and it does have a satellite conference in Shenzhen, China. However, it is definitely NOT the IEEE ICWS.

If the Chinese Computer Federation conference list does not specify that it is the IEEE ICWS conference, then there is some possibility that the Services Society conference is acceptable to them. In that case, since both the organizing committee for the Services Society Seattle conference and the IEEE San Francisco conference have members at Chinese universities, perhaps if you email them they could help you understand which one is recognized by the CCF.


Consequences of a master thesis with errors I finished my master thesis 6 years ago, since then I gained a lot of experience on my field and also in academia. I can say that in that time I was a "child" , very inexperienced. I think that also my advisor did not help a lot, and I tried to do  a lot of things by my own, without someone to really review my things. Despite that, I still received a good mention by the master committee on final results.

Some days ago, I just decided to read part of master thesis, and I just realized a big nightmare coming. I identified a lot of typos, some references errors, and some improvements that I should do, but I did not. The worst thing is that I wrote a paper to a conference with my master thesis results (and some errors are there). The conference is not a high impact conference, but still the paper has my name and is available for everyone...

As I am finishing my PhD studies for now, I am concerning a lot that situation. My dream and goal is to pursue a Professor career. So, my questions are: What are (or could be) the consequences of a master thesis and paper with errors (some minor and some major) in academia? Revoke degree after years by errors in master is possible ( I am concerning a lot this situation that I thought on it)? How can I minimize it (I thought that I can put a fixed version of my thesis and paper in the future, for instance on my personal website)? How those errors can impact me in a Professor recruitment process?

Sorry for the long text, I am blaming myself all the time, and really afraid about the possibilities since I know that Academia career is really competitive.

Relax. Nobody is going to take away your degree for some honest mistakes in your masters thesis.

The errors you describe are not even that serious

  I identified a lot of typos, some references errors, and some improvements that I should do, but I did not.

as it sounds like they don't even invalidate the main results of your thesis. But even if you found a major error that completely invalidated your thesis results, if it was a genuine mistake and not the result of misconduct, you would not have to worry about losing your degree.

You can minimize the impact of the errors by putting an updated, corrected version on your website (and marking it as such). Not because otherwise there would be serious consequences, just to correct the record.

The best way to minimize the impact of your early work (thesis and paper) is to make sure that your most recent work is high quality, so that it is obvious that the mistakes in your early work are due to inexperience and don't reflect the work you produce now.


NSF postdoctoral fellowship eligibility requirements I am about to receive my PhD and would like to apply for an NSF postdoctoral fellowship.

I have two questions about the eligibility requirements that I haven't been able to find the answers to online:

I have already secured a postdoctoral position with a mentor. I was offered a postdoctoral position and will start shortly after the fellowship application deadline. The NSF application materials seem designed for people who will start in the Summer or Fall in the year after the application. Does this affect my eligibility for the award?
I will technically receive my PhD after I begin my postdoc. Due to scheduling problems I've had to postpone my official thesis defense until after I begin my postdoc. However, I will have defended by Spring 2015, well before the award would be distributed. Does this affect my eligibility?

The eligibility requirements are stated in the solicitation.  It certainly sounds like you will be eligible.  If you defend in spring of 2015, then you can apply in falls of 2014,'15, or '16 to start the following summer (in fall 2017, it will be too late, since you will have had your Ph.D. for two years on Jan 1, 2018, and 2018 would the "year of the award). The only things that can make you ineligible are: losing your US citizenship (presumably easy to avoid), serving as PI on an NSF grant (again, not a huge danger), and being offered the fellowship and turning it down.  (I think there is also some general stuff about drugs convictions, etc.)

Taking any kind of job will not make you ineligible, though it's possible it could influence the way your application is looked at (for good or for ill, I can't really say).  You can apply with your current job as your site, or with a different one.  I don't know many examples of people getting the fellowship after they first graduated, though of course, 2015 will be when you first graduated, so maybe that's not an issue.

Just a general comment: if you have questions about NSF awards, ask the program director.  They'll be the ones who actually interpret the rules, so they know better than random people on the internet how they'll be interpreted.


Currently taking graduate course as an Undergraduate I am currently taking graduate algebra as an undergraduate (junior) in University of Toronto Mathematics Specialist, and I just took a midterm today. Since I am an undergraduate, the grade matters. In order to get a good chance to go into graduate schools for Math in Canada and USA, do I need to get a A or A+ in graduate courses as an undergraduate? Or, is A– alright? (In my university, C and below are considered fail for graduate courses only) Or should I get at least an average grade on that course as an undergraduate competing with graduate students, instead of worrying about actual letter grade? (this is about absolute vs relative too)

In order to get a good chance to go into graduate schools for Math in Canada and USA, do I need to get a A or A+ in graduate courses as an undergraduate?

I don't think anyone cares too much about the distinction between A and A+, since the standards for what merits an A+ vary so much between universities or even individual professors.

  Or, is A– alright?

This is a little more meaningful, but it's not likely to be the deciding factor.  If you have a great application otherwise, nobody will care about an A- or two.  If your application is not so compelling, getting straight A's won't help much.  This could make a difference, but it's really a low-order effect.


What we should present in defence? I am going to present my thesis in the next week. I am in a confusion what I should present in the final defence (1 hour). How much into detail I should go. I have successfully given my pre-synposis. I am currently thinking about the thesis defence. To me it appears I should focus on my work rather than talking about others work. I have been told that in the defence there will be few members from other discipline. Taking this thing into consideration I think I should first convey the high level idea and then go into the detail. So that everyone in the panel have idea about my work.

Although this answer comes too late for OP, it may help others. If it is permitted at your institution, attend defense presentations of others before you must prepare your own.  As others have said in comments or answers, thesis and dissertation defenses are highly variable and depend on the customs of the country, the institution, and even the individual department.  Very best is to attend presentations of students whose committees are chaired by your own committee chair.
If you aren't able to attend a defense or two, try to talk to one or two people who successfully defended recently.
In either case, take notes, plan your defense, then review your plan with your advisor or committee chair.  They'll appreciate that much more than an open-ended "What shall I do?" type question.
A word of comfort: Your advisor or chair should never let you reach the stage of defending unless you are ready.  I know there are exceptions to that general rule, but your advisor wants you to succeed, not only for yourself, but also because an advisor looks bad when students fail.


Which tense to describe the analysis on a dataset? It is normal to analyze a dataset in a paper. I am not sure when to use present/past tense to describe how you analyze the data and what your results are. Here I use a short example text to illustrate this:

  We analyze the epidemic spreading dataset in 2008. Let I(t) represent
  the number of individuals that are/were infected at t. I(t) is/was averaged between 1am and 3pm when the spreading is/was stable.  Our
  analysis shows/showed that the epidemic has/had infected 100
  individuals, and most of them are/were in EU. The results show
  that on average each individual has/had three neighbors.

Could you please help decide the tense of the verbs in the above paragraph? It would be good if some explanation is provided.

I ask this, because, I see in many papers, they use the present tense for analysis results of an old data. I am not sure that is correct.

In a case such as this, you need to use a careful mixture of tenses, because sometimes you are speaking about an event which is in the past, and sometimes you are speaking about an observation about that event, which still holds in the present.

Thus, in your example:

  We analyze the epidemic spreading dataset in 2008. Let I(t) represent the number of individuals that were infected at t. Our analysis shows that the epidemic had infected 100 individuals, and most of them were in the EU. The results show that on average each individual has three neighbors.

Notice that the facts about the events that occurred in 2008 are in the past tense, but the analytical results are in the present because the analysis is still showing these facts right now as the reader looks at it (I am assuming the "on average" result in your sample is a general conclusion and not just a fact about those 100 individuals in 2008).


Should I send a second reminder for recommendation letter? I already sent one 5 days ago and she said she was working on it. However the deadline is really close now (30/11). Should I send a second reminder now?

If this is for an application for a US graduate program, the due dates for the main application are stricter than for recommendations. The admission or review committee likely won't meet until next year. I had an advisor that had a habit of submitting these things a few days after they were supposed to be submitted and it was never a problem. Since they acknowledged your reminder email, I suggest you leave them alone. If you arrive at the due date with no submission, then you can send another polite reminder if you feel like its necessary.


Part-time work and extra-curriculars on Master's application CV to explain an average GPA? When applying for a Master's, I know that extra-curriculars and part-time work barely count for anything in your application. However, say I have an average GPA but my CV shows that I was busy with several other non-academic activities during my degree, wouldn't that show that my GPA is not an indication of my full academic potential? I mean if I'm able to maintain an average GPA in a rigorous STEM course while working part-time, playing in a sports team and participating in many other things on and off campus, surely I will perform better if I just focus on my course?

Your GPA is already just one metric that may be considered in an admissions process. If you're asking whether your extracurricular activity (employment included) will be considered enough to counteract a lower-than-expected GPA, then the answer is likely No, as it's much easier to compare applicants using quantifiable values than in terms of time spent on non-academic pursuits.

Perhaps the more important consideration is how the outside activities and work influenced your development as a student. Some of these can already go on your CV, especially if your work has any relation to your studies at all. Sometimes it's enough just to prove you can accomplish self-defined goals or learn quickly. Any accomplishments you can note on your CV can be helpful. Noting that you've been part of a sports team won't directly account for a low GPA but it hopefully improved you as a person - if it has occupied such a large part of your time that you believe you would have had a higher GPA otherwise, then perhaps it's worth mentioning elsewhere in an application, such as the cover letter.

The extra-curriculars don't matter on their own, but if they're relevant to who you are, they may be selling points on a grad school application. Your accomplishments as a student will still be the primary focus.

See also this previous question: Do non-academic extra curricular activities matter for grad school applications?


How to handle intellectual theft by a colleague My question is somewhat related to this one, but with the additional twist that my superior, who could be handling the case, as well as some of my friends, are implicated.
A colleague of mine asked me to do some data analysis for her, which I gladly did. She has published that analysis, even using my own wording, under her name, and also added a number of other colleagues, including our superior, to the list of authors.
Knowing my superior and the other colleagues, I don't believe they wanted to wrong me. They were probably not even aware of the true authorship of the analysis. But, as co-authors, they formally share the responsibility for the publication. I don't want to cause them problems, so how should I handle this?
Edit:
Before the publication, I happened to see the draft and pointed out to the plagiarist that my name was missing. She said she'd add it, but she didn't do it. I don't see a point in discussing it with her any further.

I am sorry to hear that you find yourself in this situation. Academic integrity is one of the central values around which academic culture as built, just as basic honesty and morality is fundamental to our society. Unfortunately, some people still lack one and the other. This situation is not your fault, but simply an manifistation that unfortunately bad things still happen in academia and in the world.
Consider doing the following:

Drop the case completely. It is damaging to your morale, but it may be the best way to progress your career in this place. Many people do it at some stage of their academic career, and report that in the long term the decision not to engage in a conflict did more good than bad to them. If (0) is not satisfactory for you, procceed to the following.
Collect all evidence that you were asked to do the analysis by the colleague (e.g. emails).
Collect all evidence that you actually performed the analysis (timestamped files, git commits, source code, etc)
Identify people responsible for academic integrity in your department. It may be the Head of Department, the Ethics Champion / Committee, etc. If you are a PG/PhD student, it may be someone in Research Office or Student Office.
Identify people responsible for academic integrity in the journal where the work was published. It is usually the Editor.
Approach people in your department to discuss the situation. You may start by writing a detailed email with well-documented chain of events. Explain clearly what you want to achieve (e.g. they retract the paper, and re-submit with you as a co-author, apologise, etc).
If you are not satisfied with the results of (5), send the email with evidence to the journal. Prepare mentally that at this stage you may be in a serious confrontation with your department and (depending on the culture in your department) it may negatively affect your chances to succeed there.

Good luck.


Is computer science worth double majoring in for an aspiring Data Scientist? I'm currently a computational Statistics major at UC Davis and have my eyes set on becoming a data scientist. I like both fields of stats and computer science, but not sure if I want to put in the effort to double with computer science, since my only career goal is to become a Data Scientist. How important are the set of skills taught in comp science and not in statistics to Data Scientists? Is it worth the double major effort?

If becoming a Data Scientist is your only ambition, I doubt you require to complete an entire major on computer science.

In a computer science major you will learn a lot more ranging from programming languages, semantics of both software and hardware design, data structure, computer architecture and organization, signal processing, AI among many others. Most of which is not even directly connected to data science. In fact a standard computer science graduation course that I've been through (both my undergraduate and graduate) did not have any element of data science in it -- no data mining, machine learning or inferential statistics. Well, most of these subjects are included as part of the current generation syllabus. But the main point is that you would learn a whole lot more than what is needed to become a data scientist if you were to take a computer science major.

Computational statistics encompasses a major part of data science, one that a computer science major would cover is lesser proportions. I suppose you would cover a considerable portion of data science just by following your current major. I've resorted to online courses and practising the skills myself to learn data science. I find this more helpful. Also, bear in mind that not all successful data scientists are computer scientists.


How to use a summary of a paper that references to another paper? Imagine we have a paper A that references to paper B by a summary, if I want to use that summary what is the best course of action?

1. Use the A summary and cite to A.
2. Use the A summary and cite to B.
3. Write my own summary of B and cite B.

As it is my first time to write a paper, I'm not sure which one is the best action and I appreciate any help.

To comment on the options you present (my comments in bold):

  Use the A summary of B and cite to A. (Don't do this)
  Use the A summary of B and cite to B. (This would generally be poor form; you want to verify the accuracy of the summary and ideally form your own understanding; in extreme cases it could be plagiarism; that said if you are merely using the summary to assist a little in your interpretation, this might be okay.)
  Write my own summary of B and cite B. (This is generally preferable)

When the particular interpretation of A is relevant, another option is to write something like:

  Alpha (2015) interprets the work of Beta (2013) to mean that ...

In general, the aim is to give credit to intellectual contribution. So if someone does a study or writes a paper, cite the primary source. If you want to summarise what it means cite the primary source.