I am struggling due to insufficient background in a graduate course and feel like a moron. What do I do? I just started an Astronomy PhD program, which due to COVID has been all-online so far. Research and my computational methods course have been going OK, but I'm being utterly destroyed by my Galactic Dynamics course. It's clear to me that I didn't have the necessary mathematical physics background going in, so I've spent all semester struggling. It's reached the point that I spend more time on the problem sets on this course than on my research and other class combined, and I can still barely do it. I've been working on nothing but the current problem set since Tuesday, without making any significant progress. I've been reaching out to the professor for questions via Slack, but as often as not this leaves me more confused than when I started. I'm starting to doubt my ability to do this, but I can't quit. What do I do?

Sounds like my Physics II course decades back.  The effort on that course was greater than the effort on all other courses combined.  Little did I know that I took it from Paul Chu, who at the time was a Physics Superstar, and he wasn't going to "just pass anyone".
Somehow I managed to get a B.  To me that B was worth more than all the A's I obtained combined.
Hard material is not a sign of you being a moron.  Perhaps you're not ready for the course; but, the entire idea of taking a course is to challenge you with new material.  The harder the challenge, the more you will learn.
Take it easy on yourself.  Try to do as much as possible without over involving your professor, but do use an hour of their "office hours" to go over problems you have difficulty with.  Be very well prepared for this hour, showing them what approaches you've tried and failed at, and can they teach you "how to approach the problem" instead of "how to do the work".
For support, find others in your class that seem to be bright enough to warrant working with, and form a study group.  If the group starts to look like an unbalanced "share the answers" group, get out of it, as it will do nothing to prepare you for the exams.
Rearrange your schedule for the reality that you'll be spending six to eight hours a day doing this class's work, until you can gain enough mastery to cut it down to a mere four hours a day.  Remember, it won't last forever, you only need to keep up the crazy schedule till you get out of the class; and, should you have to repeat the class (which is common for such classes), you'll not be under as much of a burden due to your gained familiarity with the material.
In short, hang in there; but, hang in there smart.


Listing languages in CV I study in a field that is not related to languages. Should I put languages that I can speak into my CV? Other than English, I can speak my native language, and two other languages that I've learned through classes (at an intermediate level).

Might it be seen as irrelevant and hurt my CV?

It is completely acceptable to display this kind of information on your CV and I can't think of any situation where it can hurt you professionally or socially. However, I would not recommend putting this kind of information on the resume you send out for specific positions (or even proposals) unless it is important to the job (or proposal) you are applying to.
I say this because your CV is a more complete profile of your professions and abilities and listing another language (preferably near the bottom somewhere above hobbies) will not be deemed as a bad thing and in many cases it can actually have a positive effect on how others perceive you. However, a resume is more tailored for specific needs and listing irrelevant information can always have a negative impact on the opposite person.

EDIT: In my personal line of work/study (computer science), speaking another language usually does not help you directly, but is is seen (by many) as an indication of your potential abilities. Speaking another language (and speaking it well) is not an easy task and those who are capable of doing so, show above-average motivation and determination.


How should I address my two supervisors (one man, one woman) in an email? I am currently doing a PhD. My professor, which is my first supervisor, is a man. My second supervisor is a woman and works for my first supervisor. The etiquette and the courtesy, at least in my country, requires to greet the female first, e.g.:

Dear Dr. Female,
Dear Prof. Dr. Male,

This sounds wrong to me, since my professor is my main supervisor. Can I greet my professor first without violating etiquette?
My PhD position is located in Germany; my research field is mathematics and I write everything in English.

Hierarchy beats Gender in Germany
In a professional context in Germany, hierarchy beats gender, at least according to the Knigge, which is an etiquette guide of nontrivial influence. This goes as far as to be gender-blind. Only rank is important.
So you would address the highest ranking person first, in this case, your professor.


Is it OK to contact authors of the research paper requesting an implementation? Given the research paper itself and having a complete understanding of the proposed system, I could implement the system on my own. If my intention is not to re-implement it myself, but rather study the original implementation(probably by the authors of the paper), I've got the following line of questions

Is it acceptable if I seek the original implementation of the paper?
Is it OK to contact the author(s), and request their implementation by giving a valid reason?
Are IEEE papers intellectual properties, that such a task of seeking implementation is entirely stupid?

That is three questions, i.e. two more than the allowed quota for a
post :-)

Seeking more information on a topic, within legal limits, is your privilege.

It is always OK to write to an author about any aspect of his works,
as long as it remains a civilized exchange (which is sometimes not
the case). It should be preferably clear that you have a technical level that justifies taking his time. This should show in the style of your request, not by listing degrees and achievements.  Just be very simple, clear, and direct.

He may reply or not, or tell you what are the limits on what he can
tell you if some of the work is confidential.

But you should first try to see what is available on the net from him
or others who may have collaborated.  People are usually friendly, but
sometimes swamped with requests, and try to save time by preparing
answers on their web site, or the web site of their organization.

So, writing is encouraged. Asking for more papers or implementation,
or experimental results, is fine. But always try to spare the time of
the people you write to, and avoid asking idle questions or using the
exchange of mail as free tutoring.

All papers, all writings are intellectual property (IP), including this
answer to you, which I happen to put under a creatve common licence,
so that you do not even notice it is intellectual property.

An IEEE paper is also IP, usually owned mostly by IEEE, but there are
many more detais to this (search the web for "open access" and "Berlin
declaration").

But the IP concerns only the text of the papers, not the ideas you can
find in it.  So you are quite free to rewrite the content of a
scientific paper (not a novel) in your own words and distribute it ...
with due acknowledgement to the original author as the creator of the
ideas, and reference to his original paper.

IEEE may own IP rights on a paper, but that implies in no way any
right on the implementation of the ideas, on the experimental results or other preliminary work conducted to produce the paper, or on derived work,
including the notes a teacher can write for his class on the topic of
the paper.

Does this answer your questions?


Are virtual machines good for reproducibility and open science? Virtual machines (VMs) provide a way for scientists to package not only scientific software, but also data, external dependences, and even entire operating system configurations, facilitating a faithful and exact reproduction of a particular computing environment used to derive a particular result.

Are VMs and other container systems used in this way actually a net positive for open science? If the scientific software used to compute a result can only be replicated in a very particular computing environment, is it useful and reliable?

I like to think of VMs, containers, and the like as allowing repeatable research. I don't consider it reproducible if the results can only be repeated by having exactly the specified VM/container used by the original researcher.

That doesn't mean VMs or containers are not useful for open science; they do provide a means for people to rapidly disseminate the computing environment within which the results were produced. They also provide a point of reference should software used change at a later date which causes repeatability issues. If open source software is used it would be possible to track down what changed and why this affected the particular results.

At the very least, VMs and containers provide a good means with which to document the computing environment used to generate the published results.


What contributions from your advisor must you specifically acknowledge in your dissertation? When you're writing your dissertation, your advisor will undoubtedly make many contributions. They might suggest literature to look into or ways of tackling a problem. Many of these contributions I assume would be acknowledged sufficiently in the acknowledgements section where you thank those who helped, your advisor above all.

But what about when you're discussing something specific, maybe trying to figure out some problem. When you're working through it together, your advisor says "What about you try X?". It turns out that X solves the problem.

In the section of my dissertation where I discuss the problem and present my implementation of solution X, should I include something like a footnote stating explicitly that X was suggested to me by my advisor in conversation? I'm interested more in the issue of academic honesty/integrity than issues surrounding my relationship with my advisor (e.g., whether it might tick him off to not be specifically acknowledged in the section on X).

It really depends on how original the contribution is.  At one extreme, your advisor might say something like "Hey, you're integrating an oscillating function times a smooth function.  Have you tried integrating by parts?"  This sounds impressive to a beginner and pointing it out could be a valuable contribution, but your advisor is just cluing you in to something every professional knows.  There's no need to offer explicit credit.

At the other extreme, your advisor might hand you a sheaf of handwritten notes and say "Here's what I tried and where I got stuck when I thought about this problem a few years ago."  If you make use of original ideas from the notes, then you should certainly attribute them to your advisor.

The tricky part is how to gauge originality.  You might not have been able to tell at the time, but looking back you'll often have a better feeling for this.  If you recognize an idea as being relatively standard, then offering general thanks in the acknowledgments should be enough.  If the idea still impresses you as you are writing your dissertation, then it deserves attribution.  If you aren't sure, you can always ask your advisor.  For example, "One of the decisive moments in my studies was when you suggested I should use Bayesian hierarchical modeling.  I haven't seen other papers using this approach, so I feel I should explicitly credit you with the idea in my dissertation.  Does that seem appropriate?"  Your advisor will either gracefully accept credit, declare that it's not worth worrying about, or offer background references you've missed.


Is it ok for one of your letters of recommendation to come from someone in the department/program to which you are applying? [See title.] It seems ok, but one might consider it unfair in some sense? I just wanted to make sure this is acceptable. (I am specifically talking about graduate school applications.)

Edit: I apologize for previously withholding some information. Here are some more details.

I am majoring in math and will have two letters from math professors at my undergrad institution.
I also have a letter from a professor in a different department ("probability/statistics") in the same institution and am applying to this department's graduate program.

Yes, this is fine.  On the whole, a letter from someone in the department is better than a random one, in that the writer is well-known to the people on the committee.  So, certainly this isn't something you should go to trouble to avoid.

EDIT: It seems from comments that the OP is applying to their own undergraduate institution.  That's it's own can of worms (many schools heavily discourage this, for good reasons, I think), but I think there's no question that you must get at least one letter from the institution you attended as an undergraduate.  It would look really problematic if you didn't (committees would assume no one there would write you a good recommendation, and thus wonder what you did to ruin your reputation).

EDIT 2: Just to comment further, given that the OP also left out that they are changing to a different department from their undergraduate degree.  In that case, having a letter from someone in the department is a big help.  I don't think the jump from math to statistics is so big, but generally it's very good to have letters from someone who knows the program and what it takes to succeed in it well, which is always tricky when shifting between fields.


Is my research statement supposed to lead to papers in top journals? I'm about to start a 2-year postdoc in math with a possibility to stay a 3rd year. This means I will probably be on the job market in one year. I am planning to apply for tenure-track research positions. As I understand it, to get a tenure-track offer I should try to get at least one paper in a top journal. When I look at my research statement, I don't think I have projects that will lead to papers in, say, the Annals or JAMS or Duke. I listed a few projects that I think can get done quickly but are basically extensions of other people's papers, the idea being that these are "low-hanging fruit." I also listed some long-term ideas but they are speculative and aren't concrete problems. If I follow the research statement I probably won't have a paper in a top journal. Am I doing something wrong?

Edit: I should also add that my letter writers all looked at my research statement (they requested it) and didn't say that anything was off.

You do the best you can do with the ideas you have.  Most very good mathematicians can’t come up with a JAMS worthy result every year, and it’s harder when you’re just out of graduate school.  You do need to spend some time on some more ambitious ideas as a postdoc, but “more ambitious” doesn’t mean JAMS-worthy and there’s luck in which ideas pay off and when there’s an unexpected connection that turns up when you’re in the middle of a project.

Yes if you want a job at a top place you might need top publications, but all you can do is do your best with the ideas you have the expertise you developed.  Most of us only get a couple publications at that level in our life.  Aim to be productive and to increase the level of your publications, but thinking about how to get into JAMS or the Annals isn’t helpful.


What unforeseen dangers might exist when choosing an open access textbook for a new subject? I must create a new module (first time to do from scratch). In the past, I've had either existing course descriptions, textbooks, etc. as a base on which to structure a module which is new for me. This time, I have nothing (blank canvas).

My thought is to use an open access textbook and some initial looking makes me believe I can find something either usable as-is or that will allow me to mix-and-match the chapters into something suitable.

I'm concerned about what, if any, dangers might not be obvious to me at this stage but might cause significant problems for the students, myself, or the department later (say, after the semester begins).

After reading this question, I still find myself wondering.

If anyone has designed a course using an open access textbook in the past, what are the key considerations which should be considered for first-timers?

It's important to check the license used by the author to make sure that it's compatible with your use of the open access textbook.

Issues to check include:

Does the license allows you to redistribute the book to your students (e.g. by putting it on your course web site) or whether students have to get it from the author's web site.  I would not be willing to use any material that I couldn't distribute to my students, because the author might pull it from the web at any time.
Does the license allow you to modify the work (e.g. fix typos or more broadly edit the work)?  How are you required to describe any modifications?
If there are any restrictions on "commercial use", does your course constitute commercial use?  Some people have argued that for-profit higher educational institutions can't use Creative Commons NC (CC-NC) licensed materials in courses.

Many open access educational resources are licensed under the Creative Commons license with varying options (CC-BY, CC-NC, CC-ND, etc.)  The Creative Commons web site has clear explanations of how those licenses work.  Many other resources have been put up on the web with no specified license.  If there's no license specified you should contact the author and ask for permission to use the material.


Are ERC starting grant preparation courses worth it? Has someone here an opinion or already experience (and recommendations) with online preparation courses for writing ERC grant proposals, or for other kinds of funding?

Yes, but this is strongly dependent on the course. For example, will the teachers give you personalized feedback on your proposal, or is it simply a course that offers general guidelines and tips? Usually the "tips" type courses are helpful if you're a first-time grant applicant without a strong external network. However, if you have colleagues who have been awarded these grants or have served on review committees and are willing to look over your proposal, their feedback will be more valuable. Especially because they will be more similar to your actual reviewers (pressed for time, not in your field, etc.).
If it's a course that includes specific feedback on your proposal, or if it's a private grant preparation service, these can be more helpful because they are more tailored. I have some colleagues who run this kind of grant editing/grant preparation service, and they usually offer some statistics in their promotional materials. For example, of those whose grants went through this process, how many were funded? My colleagues' success rate is about 80% at the moment, so clearly these kinds of services can really improve your chances. However, they also require substantially more money that may not be reimbursable by your university.


How do I get records proving that I was an Erasmus exchange student? I was an Eramus interchange student in the UK for my final year project 20 years ago.

Whilst I have that in my CV, and even have in the Internet my final report in an old site of mine, I do not have a single proof in writing I was ever there. Even while there, I never had a proper student card, but just a provisory piece of paper.

Come to think of it, the most tangible documents I have ever with me even at the time where the check with the grant money and the programme evaluation form.

My tutor is probably retired. I recently emailed my project supervisor with this doubt, he did not answer.

Note that I myself work in Academia nowadays, and although it is in a different city and unrelated University, I might also pose them this doubt.

The document would come in handy someday, for instance to help me getting the nationality of my wife. What would be the best course of action for getting it after all this time?

I ended up contacting the Academic Services / Student Requests Information Team by email.

They said immediately they would provide a document, and they delivered; Pretty much professional and very attentive people.

They found my records from 20 years ago, but not my grades. Fortunately my dissertation grade is not so important, as I have it in another set of documents.

I already have the document in electronic form, and they also sent a paper copy.


Chances of getting into a CS PhD program from a math/engineering background? I have an undergraduate degree (B.E.) in Biomedical Engineering. Right now, I am working on a M.S. in Applied Mathematics and Statistics and will be done pretty soon. I have very minimal research experience, and no papers published. I will be working in industry for the next year (since I didn't apply for any PhD programs this application season). However, I would like to get into a PhD program as soon as I can.

My interests are in the field of AI and Machine Learning. From the research I have done, most AI/ML research is done by Computer Science departments; so that is where I must head. What are my chances of getting into a good AI/ML program coming from a different background and no relevant research experience (although I have no direct research experience, I follow the literature and learn as much as I can on AI/ML in my free time)? What can I do to best increase my shots of getting into a top CS department known for AI (Stanford, Cornell, etc)?

Instead of working for a year, maybe I should seek a research assistantship at an AI lab (less pay, but if it can get me into a top program then so be it)?

Any advice is appreciated!

High GRE scores open the door (quantitative reasoning and to a lesser extent analytical writing).
Math / Statistics background will raise eyebrows.
Creating a portfolio of projects to show the department that you can in fact code, will get you assistantships.
Be pro-active but not a pest.

When applying to a university, you might want to peruse the various web pages of faculty to see what fields they are publishing in. If you find a field that piques your interest, or that you've done some similar work in, contact the professor directly and seek advice. Be careful though. I get emails all the time with prospective students phishing for assistantships. I can tell when it's a bulk / canned email that was sent out to a ton of faculty just looking for someone to bite.

I would start with a personal tone:

  Hello Dr. ______________ I was looking at your work on _____________ and wanted to let you know that I am very interested in this research topic.

It probably would be even better if you could tell them that you already applied to their university, but I understand that you would go broke with all the application fee's if you applied to every university that peeked your interest.

Briefly explain what your seeking (cool research, with possible assistantship). Be patient, you might not get a fast response, if ever. These guys are busy, and probably getting a bunch of emails as well. Start early. At least a full 12 - 18 months out. This way you can narrow down your choices and not have to worry about application deadlines coming up too quickly.

Communication skills can be big. If your comfortable in front of a classroom, and your english skills are good, maybe looking for a teaching assistant position could increase your chances. This does mean you won't as much research, but at least your getting your foot in the door. This will give you more opportunity to show a particular faculty member what your worth.


Why do Australian universities have higher employability than that of British in QS Graduate Employability Ranking 2019? QS Graduate Employability Rankings 2019

Why do Australian universities have higher employability (rank) than that of British ones in QS Graduate Employability Ranking?

Know what's being ranked. Read the methodology. The QS graduate employability ranking uses:

Employer/Student Connections (10%). How many employers go to campus for presentations etc?

Alumni outcomes (25%). They take a list of the most influential people and sort them by the institutions they graduated from.

Employer ranking (30%). It's sourced from a survey sent by QS to employers asking them where their best employees graduate from.

Partnerships with Employers per Faculty (25%). They find out which institutions are collaborating with employers to do research.

Graduate employment rate (10%). How many graduates are employed after one year?

Then go to the employability rankings again and sort by indicator. We're comparing the University of Melbourne and the University of Sydney to the Cambridge University and Oxford University (the two highest-rated British universities).

Sydney's scores are: 99.3 / 93.3 / 94.4 / 98.4 / 97.9
Melbourne's scores are: 78.5 / 92.6 / 97.9 / 95.4 / 98.1
Cambridge's scores are: 89.6 / 99.9 / 100 / 88.9 / 75
Oxford's scores are: 81 / 100 / 100 / 89.6 / 69.5

Note that these numbers are very likely expressed as a fraction of the top performer. In other words 69.5 employment rate doesn't mean that 30.5% of Oxford's graduates are still looking for a job after a year; 94.4 employer ranking doesn't mean that 94.4% of all employers think Sydney's graduates are good.

The British universities handily lead in the alumni outcomes & employer ranking categories, and do (relatively) poorly in the partnerships with employers per faculty & graduate employment rate categories, while the employer/student connections category is more mixed.

Draw your own conclusions. Some factors to think about:

Taken at face value, this implies that employers value Cambridge & Oxford degrees very highly, but they still find it hard to get a job (an evident contradiction).
Further, if they actually do get a job, they are more likely go on to do really well (of course, this only applies to the very peak of the distribution).
Do you care about which institutions are collaborating with employers to do research? This will vary by student. Presumably if you're looking to do research in industry, this will be very important to you, while if you don't care about that then it doesn't matter.
Are you seeking a local job? If so, then campus presentations are a great opportunity to network. If you're not looking to get a job in the city/country then they probably aren't really relevant to you.
The UK recently went through and is still going through some political upheaval. That could've had an impact on its university graduates getting a job, especially if they're international students.


How do you become a referee for a math journal? I am in the early stages of my career (post PhD) and I would like to referee papers to (1) get the experience and (2) be exposed to unpublished writing to learn to improve upon my own writing.
How do you become a referee? Can you register?
Edit: I'm not asking about reviewing.

Generally, you would publish in Journals, and the Section Editors will descend on you like vampires in search of fresh blood asking you to do reviews.
If you have trouble breaking in, talk to a mentor, and ask them if they can recommend you to the section editors (in place of themselves) for any refereeing jobs that are really up your alley.


How do we test in academia, if a research area is a money burning machine or non/pseudo-scientific? In the recent decades more and more meta studies were undertaken on the reproducibilty rate of distinct research areas like psychology, biomedicine...

The results are probably shocking to most outsiders and especially scientific laymen, not so much to the insiders I fear. But if over 70% of the studies cannot be reproduced, I'm wondering what does this mean for such fields, how do you judge this number, especially I'm wondering how such reproducibilty rates are and have developed the coming/past decades:

The amount of published falsehoods grows further and further until it converges to a final reproducibility rate (assuming the number of publications stays at some point in time constant per year), though at 30% I'm wondering how to qualify a scientific area, non-professional, pre-/pseudo-scientific or may this value even be acceptable or quite good for some branches?
shoudn't there be somehow an acceptable minimum of the reproducibiltiy rate, otherwise most of the funding money is just burned without amortisation in industry products. You could from this perspective argue most of cosmological research has no value as no industrial output, so the main criterion must be the reproducibilty rate or something like the successful experimental validation rate of new theoretical hypothesis. The latter works in physics and mathematics probably in a good way, I'm not sure what the reproducibility rate in experimental physics is, as I don't find meta-studies and there doesn't seem to be a problem of largely burning funding money and mostly industry is funding R&D in physics. Medicine on the other side is largely granted by the public and this industry and its economic prosperity strongly depend on public funding and may be not even self-sustaining without it in contrast to car industry etc.

It is clear that there has to be a minimum of the reproducibilty rate, as scientists are not perfect, unprofessional, biased, misconducting etc.

But how can we estimate what a low, normal, very good reproducibilty rate for a distinct scientific branch is? Is there any literature, answers, methods, heuristics, estimation on this apart from single meta-studies? Meta-Meta-studies over several decades?! Is it a economical or a sociopsychological estimation? Estimating the complexity of distinct experimental branches by dimensionality (number of variables of the system) X experimental steps X experiment duration X ratio of experiments conducted by students/scientists X ...

Shouldn't it be possible to derive here measures for distinct branches with <10% accuracy if the economical sciencs try to model much more complex systems?

This raises several difficult discussions. The TL,DR is: Don't worry.

First, one thing is to talk about reproducibility when studying physics or chemistry. If I can make electrons eject from a metal sheet by some method, so can you, as long as you "reproduce" my metal. In this case, both the matal, a source of light and some electron detection device should be accessible for another research group without much difficulty. And usually there is an underlying mathematical foundation that allows one to work around difficulties in the reproduction. I.e. if you don't have the same metal or the same light source, maybe you can figure out a different material and light frequency that works for what you get. Are you reproducing the my exact claims? No. Are we jointly validating (or attempting to falsify) the same general thesis? Yes.

If however, I'm trying to make a medicine study, and my sample is the whole population of New Zealand. How can you expect to reproduce my results? Will you test the same people again? Will you try to test the whole population of Equador or some other country? Or maybe a whole province in China? If you get discrepant results, will you claim my results are false?

If you like to think like a mathematician, Imagine I give you a black box that presents a different number every time you touch it. And I give someone else a black box that looks exactly the same. For some reason, both of you believe that the sequence of numbers they generate is very important, and you start conducting research on it. With enough research, you are going to conjecture and decide that it is impossible to falsify that both black boxes are embedded with a random number generator, but each one has been initialized with a different seed. You'll convince everyone of this thesis if you are both able to record all generated numbers, figure out the random number generator algorithm and the seed of each box.  To make things more complicated, imagine the generated numbers are actually feed to some kind of Infinite Impulse Response filter. And that the coefficients for each box are different, and also need to be discovered for the scientific truth of the box to be uncovered.

Now, try to imagine that while you are touching the box, recording numbers and researching its behavior, you also need to publish results.

First, raw data is hard to publish. You can put it on a public database these days, but it does not constitute an actual publication. Remember: publish or perish. Even if you do, your date is not the same os the other box. If a researcher on the other box publishes his data, that is widely different than yours, would you claim that he is reproducing his experiments?

Now, as part of scientific investigation, you will try to figure out facts about the numbers. Are they correlated? Is a moving average nearly constant over a wide enough window? Is there a trendline? But the numbers are random, so what if none of this properties are matching? Do you actually expect that reproducibility will be possible when studying such machines?

And see that there is indeed a well defined and deterministic behavior for this machine, but it depends on parameters that are unknown, and are not replicated in the different boxes as they're found. Yet, nobody knows this for sure as the search goes on. People might start to conjecture that the numbers are absolutely random, that they are affected by room temperature, time, weather, vibrations and so on. Some people will claim that there is absolutely no logic to these numbers, and that the boxes are completely different.

Given this scenario, try to answer your own question. One day, someone might be able to discover the algorithm, filter structure and a method to find the parameters and seed. This would mean that every box owner can reproduce the method, the results, and start forecasting the future outputs of the box. Until them, nobody has found the truth, and even incomplete parts of the discovery would be very hard to prove by themselves.

But so what? Should people not publish anything? Do we consider this box to be pseudo-science and not fund it? (To be clear, the hypothesis is that everyone is convinced those boxes are important, though if we actually performed this experiment, they wouldn't be).

But this enters another question: How do you measure return on investment on scientific research? And should some return be expected at all?

If PhD students pay nothing for the classes they're taking, equipment they're using and still receive some stipend. I'm pretty sure they have to deliver some result. Otherwise, we'd be talking about social welfare, not about research funding.

Notice that there are companies, trusts, funds and other profit oriented organizations that will promptly provide money for a high enough chance of return above the risk-free rate. Then again, I've rarely heard of this financial sponsoring within academic environments. There are cases were companies fund a few scholarships mostly to ensure that enough people will be trained on a certain field of knowledge, where "acquiring existing knowledge" is a goal different than that of "performing research", which is "developing novel knowledge".

People often justify that spin-offs or unprecedented applications justify research which initially is pure science (rather than applied science). Every time someone says this to me, I ask them if they've ever developed or at least read a business plan. Usual answer is "what is that?".

As far as I've been told, no university earns money with patents. Not even MIT, or Florida University (which I heard was the highest patent-earning university in the US).

There is the case where after 20 years of research, only a few more two are needed before a commercial product is developed with the technology. This a matter of science maturing into technologies, and while people love to tell stories of unexpected success, there is a survivor bias applied there, as nobody enjoys hearing about failures.

Then again, a few fiels do have expected sigma-levels, quoting this article:

  " For publication in Physical Review Letters, for instance, 5-sigma results are generally called 'observations' or 'discoveries,'" (...) 3-sigma results usually warrant calling the result "evidence" of a new particle, but scientists need more statistical certainty to include terms like "discovery" in a technical paper.

As you may see, this kind of requirement, which falls into the lines you've probably expected as an answer is specific to a journal. I doubt it is field-wide standardized.

So, if you are the editor of a well-respected journal, you can impose your classification system (what constitutes evidence vs. discovery). And you can choose to accept or reject papers based on how convincing the evidence with respect to how feasible it is to obtain evidence at all. But don't expect everyone too agree with you.

Also, note that there is this underlying belief that observations and/or physical phenomena have gaussian-distributed fluctuations. Which may or may not be the case for the boxes example. For arbitrary distributions, a bound on the probability of a value deviating so many times the standard deviation can be given by Chebyshev's Inequality, which provides a much more loose bound. Even 5 sigma for Chebyshev means only 96% chance. But hey... Where did sigma even came from? Did you estimate it? From the data? What is the certainty you have on estimating standard deviation?

Also, I've mentioned survivor bias, there is the obligatory mention of XKCD's Green Jelly Beans Discovery.

To make my point clear, I think a lot of research should focus developing methods, which in turn may find their validity by usage, or how often then can find information that resist falsification with available data and experiments. In turn, many methods create new kinds of questions, rather than answering previously conceived questions.

However, your question seems based on the mindset of answering laymen questions "with science", which I believe is problematic in itself.

For starters, science requires precise language, which laymen don't speak, laymen (and myself on my daily life) ask simple, but likely ambiguous questions whose answers they might not understand. If I ask: "Can every even integer greater than 2 be expressed as the sum of two primes?", I'm asking a simple question, but note that "prime" in this sense is a technical term. You have to study a bit before understanding it. And If someone asks me this question, I can randomly answer "yes" or "no". But that is is not "answering with science" (though, granted, I'm talking mathematics). Despite considerable effort, this question remains an open problem. So if it ever gets solved, I would expect years of training for the answer to be understood.

To give a contrasting example: If I ask "Is cabbage good or bad for my health?". How do you measure good and bad? Those are un-precise terms. And also, I've asked "for my health". So, we may simply not have tools to infer that whatever finding you had for a test sample is applicable to me. Even then, if you manage to "test goodness of cabbage" for a subject's "health", as long as there is a single discrepant result, you should not extend your answer to me. And notice: I want a "yes" or "no" answer. I don't want you asking me about my colesterol, allergies, blood type, hours of sleep, other eating habits, type of cabbage, weather and common diseases on my city. I may not even know how to answer these questions.

But, if you could isolate all these variables, run a DNA test on me, maybe you could identify certain diseases I'm less prone to develop if eating cabbage regularly. That is insightful, that is scientific. And it requires you to collect much more samples to determine all variables that need to be isolated. Notice that those variables are part of studies that may reach "inconclusive results" by some sigma-level metric. And this goes back to my argument, that data and methods need to be developed, even if no conclusion is in sight.

Finally, we have the problem with corrupted/falsified data/observations due to researcher's mistakes and or poor ethics. Honest mistakes are usually random and uncorrelated (though there are questions about common misconceptions in several fields spread out SE sites). They are the main reason some people argue there is a need to reproduce results.

But fake data of fake is a different problem. Deliberately fake data or results are no different than a deliberate false claim. However, is it really a relevant issue? A good journal should demand reproducibility means from its potential publishers, so fake data may at best waste the editor's money investigating false claims. Assuming that fake or irreproducible research does not find it's way to top journals, and if it does, it should be relevant enough to draw enough attention of people equipped to debunk it in a few years.

Do you actually trust sketchy journals and small conferences? Anymore than laymen news sources? And here is the laymen problem again. He reads news outlets. He expects them to translate complex "scientific discoveries" to his language. He doesn't even ask for the original sources and proper citations. He might as well believe a hoax shared on social media as much as he believes an actual scientist. But this a problem with basic education, not with science itself. Implementing scientific conclusions on daily life is also not a straightforward process. That is, there is a long way between researches finding out that "cabbage is good for health" and nutritionists actually start recommending cabbage for patients. There is a reason why one should prefer talking to a specialist despite the fact that information is widely accessible. There is a big gap between having information and knowing what to do with it.

But what if... These false claims become medical products? That is why more than scientific journals, we need regulation agencies. They are the guardians of whatever methods for reproducibility or further proof should be required before alleged discovers become accessible for a broader audience.

After all this discussion, has your question been answered? I'm guessing you think "no". But then again, aren't you trying to ask a scientific question with a laymen mindset here? Even if there was a methodology to assert a sigma-level required in each field before publishing conclusions, would it be reasonable to systematically reject publications based on this criterion? I'm inclined to say no.


Applying to masters after failing one I recently graduated with a degree of BSc in Physics, after failing a masters in physics due to depression. I have been interested in data science during my undergraduate studies and have been trying to find a job as a data scientist or machine learning engineer, but after several months of rejections, it's clear that companies don't want someone with no experience or a relevant degree. So I am pondering the possibility of applying to a masters in data science or machine learning. Is reapplying masters frowned upon? Any advice on how I can convey to the professors that I can complete the degree despite my failure?
Update: I have disclosed my situation in my personal statement. My referees have also promised to write strong recommendation letters. It all depends on my performance in the interview now, if I get one.
Update 2: I applied to UCL, KCL, Nottingham and Bristol. I received offers from all apart from UCL! I accepted the offer from KCL.

I can only speak for the UK, but in my opinion there's no issue about applying for a second master's degree. I knew two people on my MSc that had already "completed" a master's degree, and for one of them it was his third.

The personal statement is always the best way to express how you feel about the course you're applying for, but if your first degree is good, and you don't need to mention your previous masters, I wouldn't bother.

If you're genuinely anxious, you could contact the course convener and ask him/her some questions about the course and then simply ask whether your undergraduate degree is sufficient qualification. (I think you should have no problem with a data science degree if you have a physics degree - just make sure you are no stranger to coding.)


What should be taken note of in collaborating with professors/ researchers? Recently, as a fresh graduate with a BSc in both physics and mathematics, I've met up with a mathematics professor from a University in my home country.

During the discussion, he asked for my motivation in collaborating and I've made it clear to him that my objective is

  1) To indulge in research during my time in industry

  2) To obtain significant research experiences during the period in which we collaborate to strengthen my application for graduate admission.

We will be working on two future research directions built on a previously published paper of his. I have been told not to share the two future research directions until the paper is published. Further, he has not commenced on this research project yet due to teaching commitments.
But, currently, I am picking up background knowledge related to this paper while familiarising myself with his previously published paper that is related to this current research.
This research looks something on the level of an Msc or honours level thesis.
He has made it clear that in collaborating, he expects results.

In academia parlance, is it clear to academics that given (2) or the fact that he expects results, it is implicitly known that I expect a co-authorship?
If it isn't, how should I broach this topic?
If it is, how do I ensure that he sticks to his word, assuming I do make a reasonable amount of contribution?
To what degree should transparency be ensured?

If this is your first real experience of research, please use this as a way of getting your feet wet, and learning how to do research.  I would advise against advocating for co-authorship with the professor in the early stage.  It could be seen as counting your chickens before they're hatched, or as a way of asking the professor to count his/her chickens before they're hatched.

Edit: I suppose you could let him know that you actually have a third objective which you forgot to mention -- to get a co-authorship.  But try to come across as ambitious and eager, rather than arrogant.  You said a primary goal is to get a strong reference for grad school.


Publishing policies for conference papers I got two papers accepted and I am just wondering how to know their policy towards publishing my papers also on CoRR. is it fine to publish the same paper in CoRR and in the conference?

Normally, it's fine, as long as you submit a pre-print and not the camera ready of the conf proceedings. You can also check these questions: Does publishing a paper on arXiv prevent me from submitting it to a non-open access journal? and Submitting a subset of my work to ArXiv.


How to review papers for conferences without comparing/contrasting each other? I'm usually assigned to review one or a couple of papers for a conference. The problem is: I only have access to these single submissions.

How can I give a score (reject/weak reject/neutral/weak accept/accept) to a submission without having access to the other papers, so I could compare and contrast the contributions and originality of them?

Even if I had access to the others dozens (or hundreds) of submissions, the question would still hold, as I surely would not be capable of evaluating all of them in the desired timeframe.
Each conference has a different impact level and overall quality of submissions. But I'm not sure if I should use this information while reviewing submissions.
I know the editor is the one imbued with the responsibility of accepting or rejecting a paper based on the reviews. But then a demanding reviewer could reject a paper, and because he reviews only a few submissions, this would clearly harm the overall process.

The goal is to score the papers on their own merits, not to rank them. There are some conferences where there are ranking systems, usually among the scored papers and done by the committee, but just like journal papers, it's possible to evaluate a paper on its own merits.

For example, if there was a paper that was technically the "Best of a bad lot", but still contained massive numbers of errors, poor writing, unintelligible graphics, etc., knowing the context isn't necessarily helpful, they're all bad papers.

Looking at their scoring metric, each one of these can be described in manner consistent with not knowing other papers:

reject: This abstract isn't suitable for the venue, is just outright poor quality, etc. and is beyond salvaging.
weak reject: This abstract isn't pathologically flawed, but would need a considerable amount of work to be appropriate for the venue.
neutral: The "I suppose" category. Filler abstracts that could use a bit of polish, or ones that failed to rouse much of a strong feeling either way.
weak accept: Promising submissions that are appropriate for the venue. While not inherently flawed, there's probably improvements they could make to.
accept: "I would like to see this accepted"

It's possible for you to get two outstanding papers and simply both think they should be accepted. Or two papers that should be rejected flat out. And things in between. Trying to rank every submission to a conference would be a massive effort, and is still somewhat arbitrary, as your ranking scheme is not inherently an objective one. Nor are you necessarily guaranteed to be qualified to rank each and every submission.

You should take into account the nature of the conference itself - for example, there are some conferences where I am considerably more lenient than I am for others, the same way there are journals where I use somewhat stricter criteria. And yes, a strict reviewer could torpedo otherwise worthwhile papers, but that's why many groups use more than one reviewer, and the probability of any one reviewer being a sufficiently strict reviewer as to meaningfully start harming the overall peer review process is pretty small.


Pure math field for PhD when planning to go into industry I'm a mathematics student just starting my third year. Since entering grad school, I have become rather firmly convinced that academia is not the place for me. The intellectual work is great, but the life side sucks. I hate the administrative bloat, the grad student/post-doc slavery system, being constantly surrounded by people with no social skills, the whole toxic self-sacrifice culture. I think I want to just go into industry, find myself some niche, or work my way up a corporate ladder. Probably in tech or finance since that's who needs mathematicians.

Problem: my research area is real pure, pretty much no direct applications at all. So, since deciding to go into industry, I've started to look at another field which I have taken a class on and is interesting to me, but I don't really know very much about yet. It's a relatively new discipline which has seen recent applications in data. There's a couple of people in my department who do related work so I might ask one of them to be my advisor. (I don't have one yet- the first two years here are very coursework heavy and this is the semester to choose.)

The trouble is, I feel like I'm starting back at square one. I fell in love with my original field during undergrad and have been going hard on it ever since. I was research-level before entering grad school, published a paper in top journal, had a bunch of ongoing side projects, and have read enough that I can easily hold conversations with professors about most topics. I know 100% I could bang out a thesis in the next three years if it was in this field. I even have an original topic based on a generalization of my earlier paper, and have made some progress on it on my own.

In my new field, I feel like a beginner. If my peers in the new field have the same level of expertise as I did in my original field, then I'm way behind. I might take longer to graduate, or have a crappy thesis. Or maybe I'll discover I just have no talent in it, that my brain is only built for original field. I don't know.

  I am very confident in my original field, but from an applied perspective it is useless. Is it worth anything to get a PhD in a pure field if you know 100% you're going into industry? Would it be worthwhile for me to attempt to change to something more relevant in which I am a beginner?

It's hard to say much without more concrete info about which fields you are talking about.

I switched from geometric analysis as academic to quantitative finance in industry pretty easily. But it was probably easier at the time in that they didn't expect you to know much, just be smart.

Being an analyst meant that the distance to applications was much shorter than from many fields. However, the PhD badge does help whatever your original field; I have helped people with topology PhDs make the switch.

My general advice to people planning to switch is to do something related to probability or statistics. There are plenty of pure problems but it leaves you well placed for applications.

(see my guide on www.markjoshi.com for more discussion.)


ResearchGate: an asset or a waste of time? There are now may different ways of increasing your research visibility: Google Scholar, laboratory/faculty/industry or even personal websites, LinkedIn, to name a few.

In your opinion, how does ResearchGate fit into all of this ? Is it really a good way of increasing the visibility of your publications and finding fellows with common research interests, or is it just a "waste of time"?

It's hard to predict how this will develop in the future. As is, I have yet to hear any positive success story from my peers. All I've heard of ResearchGate  are complaints about their invitation spam.

If you sign up (or are already signed up), make sure to carefully check all settings. There are some "hidden" settings that will repeatedly send out "Invitations" and even "Invitation reminders" to your peers (make sure to disable this right at signup time, before "claiming" a single paper!). You can imagine that if your peer gets a dozen of them each week, he will get annoyed. And ReseachGate uses your name for the invitation, so he will get annoyed at you.

Before "requesting" a paper on Researchgate, make sure they have signed up. Otherwise, use regular email (even better, just use regular email right away!)
When you request a paper from a non-user, they will be sent an invitation letter, but they cannot answer without joining: researchgate does not include contact information in your request!?!

Other than that, I have the impression that ResearchGate is mostly used by students, not by established researchers. Therefore, do not expect much high-quality interactions to happen.

Most of my peers that were on RG once, seem to have left. This is not a good sign.

If they don't manage to get high-quality interactions happen soon, their name may end up remaining associated with spam. They may have been too aggressive at boosting their user numbers quickly. The overall idea is good, but they need to find a way to get quality contributions, not only quantity. As is, I see them only strong on the quantitative side so far...

Honestly, in my opinion there is one thing really important for the visibility of your research:

Make it available. Publish with open access, or a publisher where you can put the preprint on your webpage. Make sure that Google Scholar indexes it, because that is what everybody I know uses for searching literature. You must get Google Scholar to return a PDF link for your article.
Make it available. Also put data sets, detailed description, source code, etc. on your web page; offer collaboration.

If you make it easy for others to build upon your work, they are more likely to do so. In particular, you must make it easier to build upon your work, than on alternatives. Making a RG profile does not really help; effectively it just means your data is spread to one more site. Instead, put a lot of information on your homepage.

Here is an example. I'm convinced that one of the reasons why he has been very successful is that you can download data and software and tutorials right on his homepage. Compare to his ResearchGate profile (I was even surprised he has one)  - it's a dead duck, absolutely useless. A Google Scholar Profile may be much more useful, as it will be linked from any article, making it easy for people to reach your other publications. Again, the same example: Google Scholar does a much better job at providing an automatic publication list for you.

IMHO, Google Scholar is a must, because the profile will make it easier to get updated on your other publications. I visit Scholar profiles quite often; in particular to see the latest and the most cited work of an author of which I already have discovered a good article.


My transcripts are not an accurate indication of my academic ability Can the absence of an official academic adviser in the department at which I finished my undergraduate studies be used in my graduate application to justify some deficiencies in my undergraduate grades transcripts?

The lack of an official advisor is most unlikely to be of any benefit as an excuse for not doing well in classwork. Presumably you have a common core of classes in your major, and you'd better have a good explanation for any significant deficits in those courses. The lack of an advisor might have led to poor course choices in electives, but you could have ended up taking the same courses even if you had had an advisor. Moreover, your advisor didn't take your classes for you; he didn't do your homework or take your tests or complete your projects for you. Ultimately, you are responsible for your performance as a student.


List of software projects on my CV I'm a graduate student in mathematics but I've done a lot of open source software projects, some of which have even won hackathons. My question is should I put them on my CV? I've seen academic CVs only contain publications, conferences etc. But, I feel that these projects are very valuable to mention.

You're correct to want to mention them, CVs often probably only lack them because the writer hasn't done them or it isn't relevant to their career (perhaps they do not need to mention them any longer to prove particular skills). My CV included my programming/computing experience and was well received as the programming I had done was relevant; I was applying for computationally demanding PhD programmes.

Relevance is key so when including the projects, make sure the skills you gained from completing the open source projects are clear to the reader. Make sure it is also clear how these same skills are relevant to your application.


Is it ok to cite someone who provided a bad citation for correct information? In a master's thesis the author provided a seemingly incorrect citation. ( Link to paper citation 4)

The information is obviously correct but in the cited paper I couldn't find any reference to the information he cited. Is it ok to cite him?

If you want to refer to his work (i.e. his results, discussion or conclusions), you should cite it.

If you want a reference to the particular piece of information he cited (the sentences describing plasma etching before his call to ref. 4), then you should find a direct source: either a textbook or review on the topic. This would be much better than a research article (or thesis), especially one badly sourced.


What do admissions like to see in online profiles? I have a website for my academic profile. The content include academic CV, a little bit about the research I have done, a technical computing related hobby of mine etc.

I intend to state the URL for this website on my academic CV for PhD applications.

Questions

Would the admission committee view the online profile of an applicant when considering his/her application?
If so, what kind of information would the admission committee like to see on it?

(Edit) Additional Information

I am applying to applied mathematics departments in the USA.

Admissions committees are looking for strong evidence of research potential; that's what your web site should give.  At a minimum, your academic web site should have

Your academic CV
A list of publications and research projects, with download links.

You may want to include a copy of your research statement, but only if you don't tailor your statement to each department you apply to.

Keep it simple.  If your site looks too flashy, you may give the impression that you spend too much time fiddling with your web pages instead of doing research.  (Cough.)

Make sure everything that you post on your web site is polished and professional.  Proper spelling, grammar, notation, and technical language counts, just as it does in your research statement.

Finally, @aeismail is correct: Do not assume that admissions committees will look at your site; some will, some won't.  Your application must stand alone.  But if you mention any publications, independent projects, or other results in your statement, point the reader to your site.  Even if nobody actually visits your site, your willingness to display your work in public will work to your advantage.


What is the approximate composition of a statement of purpose in Mathematics? I am writing a statement of purpose right now for US graduate schools in mathematics, and am I bit bogged down on figuring out the approximate ratio of my background versus my actual research goals. I currently have three paragrahs of background, mainly involving seminars and projects, and a discussion of the non-traditionality of my situation, and one of my actual research goals, and I am trying to determine if this is what is desired? The only source of examples I could find are here, and I am not sure if these are actually good examples of what is desired.

Edit: I keep using the wrong word since statement of purpose and Personal statement means different things to different departments (for example, Northwestern seems to want only one essay, which they call a personal statement)

When I review graduate applications, the statement serves three purposes:

If you are not a native English speaker, it may tell me something about your English proficiency.  However, it cannot be relied upon too heavily since you may have gotten at least editorial help from a friend or even a professional.
More importantly, it is an opportunity to demonstrate that you know something about a research area that interests you.  This is more important for PhD applications than for MS applications, obviously.  If you have already done some research, hopefully your recommendation letters speak about that.  Your statement can sometimes be useful in showing that you keep up with some research area even though you haven't yet contributed to it.
Finally, your statement can address issues in your application, like a period of low grades related to something in your personal life, a gap in your schooling/employment, etc.

There is in my opinion no correct ratio of personal versus research components; it depends on the individual.


What is a Brown Bag Seminar? I have seen several questions and also announcements that mention the term "brown bag" seminar. I understand that in American English "brown-bagging" means bringing your own lunch:

  the practice of bringing one's own packed lunch to work.

I would like some sort of a definition in academic setting.

It means it's held around lunchtime and they aren't going to be providing lunch, but you are welcome to bring your own and eat during the event. So the event is somewhat informal.


Confusing accusations and mixed messages with lecturer, what can I do to resolve this? I am an undergraduate student in my final year on a Computer Science course. Every week I assist in a 2nd year (undergraduate) class that I took last year on programming. The programming class covers a range of languages, tools and topics, and is divided between a number of lecturing staff. There is one other assistant other than myself who helps in the class.
As an assistant in the practical sessions of these classes, I will often be chatted by students on Teams (course is currently virutal due to lockdown) for help in completing the practical work. I help by:

Giving links to related work (such as Mozilla/specific library documentation/stackoverflow)
Explaining tasks that are too complicated
Look over (but not add anything) to student's code
Giving broken down explainations of how to complete a programming task in technical language (eg.- "You gonna need to write a function that adds x to y...")
I should stress that I never give away answers or actually type any code myself.

I am currently unsure of what to do with the current lecturer in this class as they are giving me alot of mixed messages, these including:

Asking where students are when only a third of the class shows up to the virtual teaching and lab sessions.
Asking not to share code with students, even when they can see that I have not given any code to the student (when the conversation is in the main module chat.)
Questioning why I'm spending a significant amount of time helping an individual student who is stuck and needs help. In this case (which has happened a few times) I leave the main call, go into a private call and do a 1-2-1 with the student.

I have gathered a consensus from a number of students that a good number do not appreciate his teaching style a great deal (he tends to read off the slides he has produced and rarely gives live demonstrations.) I have generally not given my opinion on this lecturer, even though I share similar sentiment from being taught by this lecturer in other classes. What is the message this lecturer is giving and what can I do?
(It should also be noted that I have a good working relationship with the head of department, who is also my tutor, and he was the previous lecturer on this module I assist in. I get the impression that my assistance is appreciated when the HoD was teaching.)

Getting questioned should never be an issue unless the demeanor is hostile. But, you probably have good answers to any questions. Just tell the prof what it is you do and assure them that you, for example, don't code for the students.
In face to face education it is advised that people even professors, normally advise students "with the office door open". You might consider getting a way to retain a transcript of any 1-1 advising you do, just as a check on any misunderstandings.
But, asking the prof for advice on what they feel you "should have done" in anything that they question might help. But keep a record of their advice.
You might also exploit the relationship with the other (former) professor who might have a conversation with their colleague to make sure that the groups are treated the same from year to year and across sections. If they can come to a common understanding and communicate it to you then everyone will be better off. They don't need to say that you've complained, and you haven't, if you just point out that there may be differences that might better be resolved. It might even be a department wide issue.
If the current professor is a novice at this, they may have some learning to do themselves about how to manage a group of students effectively. Especially online.


How can a student improve his attentiveness during lectures? I'll be starting my 4-th year of post-secondary education, in a few weeks, and during my first 3 years, I've had problems with attentiveness. My latest problem was last year, where I don't think I've been able to follow a single lecture during more than 30 minutes out of the 2 to 4 hours (There is one break during the 4 hours lectures).

I don't think it's my teacher's fault because it has always been an issue with me and, for instance this year, even though many students such as myself had problems with attendance and attentiveness, about 25% of the students felt the lectures were satisfying and managed to follow most of them.

It might be due to the fact that the courses are not always, the one I would like to attend, but I won't be able to chose until my 5-th year so this option won't work for me.

The thing is I've managed to fall through the cracks so far by simply working by myself with the textbooks we were given but I now realise it was a mistake, first of all because several times the textbooks we've been given were either obsolete or not related at all to the knowledge and skills we were actually expected to get and a few times we were simply not given anything besides the lectures.

I usually get enough sleep, 8 to 10 hours. I also play sports 3-4 hours a week.

Second of all I'm pretty sure my understanding of the course was pretty much limited to me getting good grades on the tests but not actually understanding the essence of the courses. I'm supposed to be an engineer in two years and I feel like all I know is how to get good grades but without proper actual engineering skills, and I'm pretty sure it's mostly due to me not having the right approach on lectures.

Basically, I would like to know if someone had the same problem and what approach they took or if anyone has pieces of advice or tricks to improve attentiveness during lectures ?

In general, if you aren't "pumped up" about major classes and not fully engaged with the lecture, you're probably in the wrong major, however, with some research, you can see that the average attention span of an adult is around 20 minutes, so your situation is totally normal.

I've struggled with ADD my whole life, so learning how to zone in while I'm zoned out was a full time job for me. I've gotten to the point where I'm pro at being attentive during lectures, here's what I've learned:

Lectures are much more interesting when you apply a purpose for them. For me I'm a computer science major, so what I did was compiled a "reference bible" of useful things that I could use in the future. Another thing I did was continuously develop a game. I tried to incorporate everything I learned into the game, it was pretty much a challenge, so that made programming lectures much more fun.
Invest in a smart pen. I swear I will never live without one of those things ever again. It kind of forces you pay attention to the lecture so that you can mark important topics down to make it easier to reference. Be carful though, it's easy to get lazy while using it, but just keep in mind that having to listen to a lecture twice isn't fun nor is it a good use of time.
Force yourself to ask questions. I ask at least 5 questions per hour before the end of a lecture, which for me is a question every 12 minute; well below the average attention span.
After you do homework, look up the next chapter online. Use everything at your disposal: YouTube tutorials, blogs, research documents, forums, etc. Not only will you be prepared for the next lecture, you'll already know what you're having trouble understanding which is huge, especially for #3. If you're book is outdated and you find new information on a topic, use that to challenge the professor's lecture. Not only will that keep you engaged, it will make you look like you actually care about what they are teaching (hopefully you actually do!) and that you are taking the initiative to activly learn more than just what they are lecturing about; nobody likes a yes-man!

Overall, just getting rid of distractions never did it for me, so the way I bought of it is "solve the root of the problem, not the effects," because technology is a great thing to have when used correctly, so give yourself a reason to pay attention. Hopefully this helps!


How do journals and conferences verify if the results in paper are genuine Most of the conferences and journals doesn't ask for code to be submitted along with the paper. How common is it to detect fake or incomplete results in such conference/journal review ? In my field of study in Computer Science, sometimes overwhelming claims are made in some papers, but since the field is still growing, how do reviewers/editors determine the authenticity of the results ? Is it common to ask for code or working demo in such cases.

Since you're referring to computer science, I'll talk about conferences. Peer review, as Dave Clarke mentions, is primarily how papers are scrutinized. But conference reviews are often on a severe time constraint. So for an experimental paper, the reviewer looks at the main ideas, sees if the experiments are convincing and handle the cases that the reviewer thinks are important, and evaluates the overall work for "general excitement". But the reviewer has no ability to check if the work is correct.

In a theoretical paper, a reviewer might skim proofs, and see if the main claims pass a "smell test". If the result is important, or surprising, the reviewer might try to verify key claims in detail, and expect the authors to argue why their techniques should work. But even there, no formal proof checking happens (that happens in the journal review).

Ultimately, there are two (imperfect) safeguards against incorrect claims:

author reputation - you don't want to get a rap for writing faulty papers
reproducibility - I might reproduce your work, or ask for your code, when writing my papers.


Statement of purpose imposes word limit, but I already have a significantly longer version. Can I submit it as it is? I am currently in the process of applying to a PhD program at a university in Canada. As part of the application, I need to submit a statement of purpose. The prompt for the statement of purpose is as follows:

A statement of purpose (maximum 500 words) that discusses your career goals, why you want to do research, and the areas of research that interest you. If you have done any original research, please describe the results, their importance, and your particular contributions. If you have specific research plans, outline them here.

I have already written my statement of purpose for other universities and all of them required a maximum of 2 pages. My statement of purpose is currently 2 pages long, with a total of 1,249 words. I have described everything that the prompt mentions in these 2 pages, but I do not think I can do that in under 500 words. Is it ok to submit it as is? If not, how do you suggest I proceed?
Thanks for the help.
Update: I emailed the University in question about the length of the statement of purpose, and it turns out that their application form was not updated. They in fact allow a 1-2 page statement of purpose. I think this is helpful for future situations.

I would think that two and a half times the maximum would be a bit extreme and leave people less than pleased.
A few extra words wouldn't hurt most likely, but what you suggest seems unreasonable.
Factor out the most important ideas and express them concisely. Consider it a test that you don't want to fail.

A note on the SoP. Don't use it to reprise what is in the CV or other materials. Don't use it to explain past problems/issues. Make it entirely forward looking. What do you expect to accomplish in doctoral study and thereafter. You are wasting words if you try to use it for other things.


I attended a poorly ranked school for my undergraduate education. Will this affect my chances for a PhD in economics? I studied math and economics at a poorly ranked university, and I will graduate in the near future. I have a position lined up at a prestigious, data-heavy research institution, where I'll work with numerous economists for a few years before applying to top PhD programs (Berkeley, Chicago, MIT, Harvard, etc.) I took two courses in analysis and did well, although per the quality of my institution, the courses were not challenging.

I also worked as a research assistant and conducted self-guided researched (not published outside my university), so I have several professors willing to write detailed recommendations for me. Apart from a few hiccups in non math/econ courses, my grades are perfect.

Will the fact that my undergraduate degree comes from an unranked institution affect my chances of attending a top PhD program? Will it affect my chances even though my experience after undergrad should speak in my favor?

As the commenters have pointed out, a lot will depend on your ability to get good letters of recommendation. However, there is an important issue to note: a good letter of recommendation does not mean the same thing as "a letter from someone famous." You can get a letter of recommendation from a big name that is completely useless, if it doesn't provide any real information of value about the candidate.

Instead, what you want are people who can testify that you are a good student, and show evidence that you can become a good researcher. Since you have excellent records, and are working with a research group before applying to graduate school, I think you have the right groundwork for getting good letters. To make sure that you do so, you'll want to meet with the people who will be writing the letters, make sure that they are willing and enthusiastic about writing the letters (hint: if they are at all hesitant, do not get a letter from them!), and then provide them with the necessary material they'll need (CV's, samples of writing and research work, citations and awards, etc.) to write the letter.

Ultimately, the only way attending an unranked school will affect your chances of getting in as a graduate student is if the admissions committee at the school you're applying to shows such a bias. If you're concerned about such a possibility, and have the resources (time, money, motivation) to do so, setting up a meeting with the person in charge of graduate admissions at the department you're interested in probably can't hurt.


PhD application: bad grade in relevant subject at master's I'm currently pursuing an M.Sc. in Europe. I am going to apply for a PhD program at an Ivy league university. I am afraid that some bad marks in the master's (especially one in a relevant subject) would affect my application.
Some background: I completed my B.Sc. in mathematics with very good marks (corresponding to a GPA of 4.0 or something close to it) in Europe and then moved to another country (still in Europe) to do a quite renowned master in theoretical physics (about 1 out of every 5 applicants was selected for this master).
Current situation: Because of several factors (moving to another country, changing subject from math to theoretical physics, different and stricter exam regulations*, having chosen to follow the most demanding courses) I got bad marks in my first semester. In particular I got the American equivalent to 2.5 in a subject which is relevant for my master thesis (and for the PhD project I would like to pursue).
During the second semester my grades improved. Since I got most of the required credits, I have time to do the specific low-grade exam again during next semester (I can also do more exams to replace the bad ones according to the university regulations).
Problem: Even if I do the exam again and get a good mark, I will know the results after the deadline of the application for the PhD. So, I can't reflect my ability in that core subject (apart from my results in other exams and from my reference letters) in the application. Would it be a problem for my application? Would saying "I did bad in the first semester but I will try to make up for it in the third semester" be satisfying for such elite universities?
I am very excited about the research topic and would love to work on it. I am just afraid that some bad marks could affect my chances to get in the PhD program.
Additional info: The PhD program would be in physics / theoretical physics. My B.Sc. was in mathematics but included physics exams (and I did more physics exams than average by choosing optional modules).
*here I mean that in the master's I had less time to prepare for exams (they were immediately after the end of the lectures, whereas during my bachelor's I had time between the end of lectures and the exam session). Moreover, in the bachelor's I had multiple retake exams and every retake had the same difficulty whereas here we have at most one retake and if it occurs it is more difficult than the first exam (this is an objective observation claimed by professors, I quote "you can't get a good mark at the retake" and "guys I suggest you to do the exam immediately because the retake will be way more difficult").

I believe it is very normal to have some drawbacks in every university experience, especially in the situation you described. I believe most people would be understanding if you explained to them what caused that specific grade (the changing masters and countries, etc), and I would explain how you are improving (extra hours of study, asking for advise of older students, learning better organization techniques, things of that kind). It's all a matter of how you expose the situation; if you expose it with a positive output, they will get a positive impression of you!
For my master thesis internship interview, one of the topics I talked about was how I in one semester had very low grades (average of 13/20) and even failed at one subject, and how I decided to turn things back around, got up on my feet and in the next semester, did one extra subject and ended with an average of 18/20, and they loved it, I was actually their first option for the internship! It's all about how you deliver the info :)


Can a person of average intelligence get a PhD in physics or math if he or she works hard enough? Or is a PhD a ceiling that they'll never be able to reach, no matter how hard they worked?

By average intelligence I mean a person who has an IQ of 100.

I recently reread Malcolm Gladwell's book Outliers, which has a discussion of this very point, i.e., IQ versus academic success.  The gist of it is that, like it or not, the IQ test robustly measures something so that a score below X will, with high probability, prevent academic success at level Y.  E.g. a child with an IQ below 50 will have trouble taking classes with other students.  I just checked the passage, and he claims, quoting from Arthur Jensen's book Bias in Mental Testing, that with high probability someone with an IQ of below 115 cannot graduate from a university with grades that will get them into graduate school.

One key phrase in the above is with high probability.  Thus no one is claiming that such success is impossible, but it becomes significantly more unlikely.  Another important point -- in fact, the whole point of this part of the book -- is that while having an IQ above the threshold seems to be important, the importance quickly disappears past the threshold.  In particular, though we may disagree whether the IQ threshold for getting a PhD in mathematics is 100 or 115 or whatever, beyond a certain level there is no significant correlation between IQ and academic success.  You might expect someone with an IQ of 175 to have a huge mathematical advantage over someone with an IQ of 125.  But this really seems not to be the case.

Let me say finally that this answer should not in any practical sense be discouraging to anyone.  If you or anyone else is wondering whether you have the intellectual capacity to get a PhD in mathematics -- you don't find out the answer by taking an IQ test, you find out the answer by studying mathematics and doing the best you can.  Someone who has a measured IQ of, say, 95 but gets a degree from a reputable university with excellent grades in challenging courses and competitive test scores: of course such a student should seriously consider getting a PhD in mathematics if they are interested, and should not be dissuaded at all by their "low IQ".


Master's thesis, should I inform my supervisor about the mistake? Please hear me out first,

I am one week away from submitting a manuscript, the deadline is in one week.
The supervisor has reviewed my thesis and is happy with my results.
He is writing a paper based on my work.

Now I used a technique in my work that is fairly different is almost never used, but it yielded good results so I went on. I told my supervisor during earlier days that I using that technique and he said it's not the norm, but whatever you do just document it. He never questioned my decision or said it doesn't work.
Fast forward 13 months, and I've come to know that you are not supposed to use that technique as it introduces some bias in the results, which is not discernable.
Now I am more than willing to inform my supervisor, but this will have a domino effect and I might have to start my experiments again, which will take more than one and a half months at least, and I have been working on my thesis for 18 months now.
Why should I not tell my supervisor:

He has reviewed my thesis and he is happy with my results.
My student visa expires in 21 days, and another thesis extension (if it happens) with the university would be whole another hassle, and I am not sure if I will even get the extension.
No money to pay the rent. this is a big one, up to this point I have survived by a very little margin, with no funding from the department, and I have no work now, another one and a half months would mean at least 70K $ which I cannot arrange.
Visiting parents after 2 years of not going back home because of masters, and short on money.
18 months of already working on a thesis, having done my very best, I am f****ng tired now. I just want to hand in the thesis and sleep in peace. Every single student in my class has given their defense I am the only one left.
I want to move forward with my life, get engaged and find a job. 4 years of master's school, with zero
funding from my department, even when they were happy with my work.
Getting rid of my supervisor for good, he was always pushing me and
pushing me, and I worked my ass for this thesis without any returns.
Not dealing with him would be the highlight of the year for me.
It is a 100+ page thesis, from a different perspective, it shouldn't
nullify all of my work.
I can always plead ignorance in the end. Since I am not from a
computer science background, this thesis is at the CS department
though, they know I am always learning or whatnot.
I can defend it, my supervisor is one of my graders, and he is on my
side.

Why should I tell my supervisor:

It's eating me up from inside. It is my baby, my work, I've put 18
months of work into it. It only deserves to be 100%
My supervisor is writing a paper that he wants to submit to Nature
based on my results, and it has that faulty method I used. This kills
me.
I feel like a fraudster/criminal.
Scared my work will be thesis work might get canceled in the future.

I would be happy if you can help me solve this quandary.

Everybody makes mistakes. What separates good researchers from poor ones, is how well we own up to them. I don't know the details of your work, but it is very possible that what you think is a devastating mistake, is an oversight which should simply be improved upon if the work is later submitted as a journal article. Note that it is very rare that a Masters thesis becomes a journal article unaltered.
You should get in touch with your supervisor and tell them that you have discovered this paper, which you think might be warning that your method is questionable under these specific circumstances. Then let your supervisor form their own opinion.


How can I migrate a library from Calibre to Zotero? I've got a well organized Calibre library, but due to full text PDF search, web clipping and basic bibliography management, I decided to move to Zotero.

I can import .bib records easily, but is there a way to have them linked with corresponding PDFs?

Discussion here suggests that you can export a Calibre catalog as BibTeX

  click the arrow next to convert books and create a catalog in bibtex format.
  ...
  (you have to select at least 3 [books])

You can then import the BibTeX into Zotero. Whether that transfers PDFs, comments, and tags depends on the ability of Calibre to export that metadata (all of it is supported by BibTeX).

A thread on Zotero forums indicates that Calibre may sometimes assign "silly dates", so you may want to look over your library after import.

  I have identified the origin of these silly publication dates (Jan. 1st, year 101): that's the metadata I have in my calibre database for some e-books, probably a default value assigned by calibre when a specific date isn't available in the imported document and designed to stand out when sorted in ascending or descending order.

Alternatively, if you want Zotero to try and find the metadata for your books, you can just drag and drop the PDFs into Zotero and use the Retrieve Metadata function to look up metadata. May even want to combine both approaches and merge duplicates at the end (by selecting the best metadata from both software).


Conversational tone of Statement of Purpose Essay I am writing Statement of Purpose Essay for University, which happens to be my alma matar.

It asks how has academic and work experience prepared me for graduate education.

For one, I already had graduate education, but so many other things, especially the intangibles in life have prepared me, or made me more mature, and receptive to higher education.

My question is, can I make the essay conversational, or does it have to be dead-on-serious.

For example, I wish to write (because my alma matter used to be called A, now is called B, see previous post)

  After earning Masters degree from A (oops, slip of tongue, I've always
  called my alma matter A) I moved 200 miles away from home to live and
  work in XYZ.

or should I write

  After earning Masters degree from B I moved 200 miles away from home to live
  and work in XYZ.

Honestly if it was me going over applications, and as long as the person answered the questions thoroughly and clearly and had all the qualifications, I would be amused and appreciate such a down-to-earth Statement of Purpose.

It certainly breaks the mold of reading stiff Statement of Purpose from kids who just come out of Undergraduate, really haven't seen the world, and really learned to lighten up.

Please weigh in. If you were going over applications, which statement would you prefer.

Don't do it.  A statement of purpose is an opportunity to demonstrate your formal writing abilities.  Jokes like this make it sound like you are not taking the application seriously.  (And I don't really think this one is funny, anyway.)

Also, be concise and to the point.  I don't see how the name of the town where you moved, nor the number of miles between it and your university, has any relevance to your application - people who are reading large numbers of such statements really don't want to spend time on unnecessary text.  Stick to information about what you have accomplished and how you believe it is relevant to your preparation for graduate study.

You can certainly highlight the fact that you graduated from this university, and perhaps mention any particular aspects of it that make it a good fit for you.  But you can do that in a serious and professional tone.


Supervising a research student with attention problems - what can I do to help the student be more independent? When supervising a research student with ADD or a similar problem, I can help the student be more productive by adjusting the way I give instructions:

Propose one task at a time: Instead of saying "Let's do X, Y, and Z" I will just say "Let's do X", schedule a time for follow-up when X is done, and only talk about Y then.
Repeat "general" instructions every time they are relevant: Instead of saying "When you take notes on a bunch of papers, make sure to cite which paper each idea comes from" and then expecting the student to do this moving forward, I will give the instruction again each time that the student is reading papers: "Look through and take notes on these papers. In your notes, make sure to cite which paper each idea comes from..."
Have the student repeat back a summary of the instructions, with ALL of the important details, after hearing them.
Review the student's plans regarding what to work on at what time, together with the student (to help with planning and make sure we are prioritizing important tasks).

With these and similar modifications, a student with attention problems can be about as productive as one without attention problems. (Without these modifications, the student keeps doing the wrong thing, and isn't productive at all.) But that kind of supervision is not really consistent with the goal of having my students become capable of independent research. For example, it's very hard for them to see the "big picture", identify next steps on their own, think about where the research is going and what intermediate steps are needed to achieve the end goals...

Obviously my primary role is as a research supervisor, not an ADD coach. So I'm not looking for generic ideas for helping an adult with ADD, or for suggestions to pass along to the student - I am looking for ideas specific to my role as a supervisor in an academic research environment.

As a research supervisor, what - if anything - can I do, to help students with attention problems be both productive AND independent?

OK. First of all, you are to be commended for having the awareness and sensitivity to take the initiative on this. LD/ADHD students tend to be treated rather badly in higher ed, so it's reassuring to see someone reach out for help.

I think the zeroth thing you should do is bring up your observations to the student for the following extremely important reason: If the student does have a disability, either the student already has a diagnosis and is worried about being judged negatively (or overestimates how well they're managing without accommodation), or the student has been struggling for years wondering what's wrong. It's impossible to address a problem correctly if you don't know what's causing it. With that in mind, I can only comment on ADHD, which is not difficulty paying attention, it's difficulty regulating attention.

Another preliminary observation is that if the student is ADHD, it's possible that they never developed good learning skills either as a direct result of the ADHD or as a result of the kind of spoon-feeding you are (correctly!) identifying as problematic. This kind of "accommodation" is often recommended for ADHD, but it's ultimately self-defeating. So you might be in a situation where the student is on the steep side of the learning curve for learning how to learn. It's tempting to just spoon-feed so that the student "makes progress," but this is bad advising, just as it would be bad parenting. So set the project scope and your expectations accordingly if this is the case. And the student might need an ADHD coach, academic tutor, or therapist to help with the logistics of just putting quality time in, which can be formidable for ADHDers.

In terms of  practical day-to-day suggestions:

Audio-recording meetings, or important parts of meetings, might be helpful. ADHDers often tune in and out without even being aware of it, so they don't know that they've missed something.
Work on the "big picture" first. This might seem counterintuitive, but the student might find it easier to recognize what to focus on once they have a clear idea of how the pieces fit together.
Ask the student to come up with their own suggestions about how to approach the project. Be open-minded about what the student comes back with.
Provide regular, structured, nonjudgmental feedback about your expectations and what is and isn't working.
Let the student know you're on their side and that you have confidence in their abilities. Anyone with a disability can tell you that the worst part isn't managing the disability, it's dealing with the low expectations and lack of inclusiveness from everyone else.

The main takeaway is that ADHD adults often do much better in more self-directed activities than they do when given close supervision. A common incorrect assumption is that because an ADHD student has trouble following instructions, then it must be the case that the student is incapable of working more independently. Which is not to say that the student should be left to their own devices, but rather that the support provided by the supervisor needs to be more student-centered to be effective (and efficient).


How to write a Ph.D. thesis Introduction chapter? I am in the process of writing my Ph.D. thesis and struggling with the introduction chapter, what to cover, what not. This is a technical thesis. The broad area is molecular simulation in statistical mechanics.

There are lots of tips available on Internet, but those are very general often. I have found few theses also searching on Google. All those tips and theses vary in style and content and it is difficult to decide which one to follow. Please provide tips on how can I write a good introduction chapter of high academic standard.

EDIT: After reading your valuable inputs, I am adding here few more points. (Thanks everybody). Your comments on these points are highly appreciated.

I have seen theses where people include background theories. On this matter, I have read that the theory should not be something which others know. Now this is difficult to predict what to include as background theory, what not. I have seen people including theories which are widely available in textbooks.
The chapter 2 is dedicated to literature review. It is also important to briefly mention background and relevant research works. Here I have found people are giving a little elaborate technical details.

My thesis, which does happen to be in the area you are writing about, took a somewhat different approach for the introductory chapter:

Motivations—why is this particular problem important
Related efforts—in what context was the work done
A short summary of the main development of the thesis (without a formal hypothesis statement)
An outline of the remainder of the thesis

The following chapter, which was also an "introductory" chapter, but more of a "preliminaries" chapter, defines all the major concepts, tools, and ideas used in the remainder of the thesis (which were derived from my published papers, and therefore somewhat briefer than what is needed for a thesis).