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Can I get a tenure track Applied Math assistant professor position in a top US college if my PhD was in a similar ranked private U.S. college?
I have almost completed my Ph.D. in applied mathematics at a highly ranked private U.S. college. I am an older student with almost a decade of industrial work experience between undergrad and grad school, and so I would prefer not to go through a postdoc before I can get my teaching career into motion. As for publications, I have two (maybe three by the time I graduate) first-author papers. I have some teaching experience as a TA in the first couple of years, and I also taught my own course here last semester. My career interests mostly lie in the form of teaching, but I would also like to conduct some good research.
I have observed hiring trends and it appears as though it is impossible nowadays (without a postdoc) to get a tenure-track position at any college of higher rank than the place you got your Ph.D. (The rank doesn’t matter that much to me, but I understand the correlation between such rankings and the quality of research (and research funding) that will be available to the assistant professor). The alumni from my program, who are in tenure-track positions, are all in lower-ranked colleges – except for one holding a postdoc position in a college of similar rank.
Would you say I probably have to choose between doing a postdoc at a higher-ranked school (then apply for TT jobs at such higher-ranked schools) or simply return to industry?

Can? Yes. Is it easy? No.

@Thomas Ah yes, thanks for rephrasing. I meant, is it feasible? I know nothing comes easy. However some things are - for all intents and purposes - not feasible.

It's unlikely given the current state of the job market that you'd be a strong candidate for a tenure track position at any institution with a graduate program in mathematics.  If you want an academic career, post-doc, get some more teaching experience during your post-doc and then consider applying for faculty positions at a range of institutions including regional comprehensives.

Unclear why it's unacceptable to you to get a job in a "lower-ranked" college/university... Even if you had followed a more conventional route, the whole game is very competitive, and it's not (statistically) reasonable to imagine, for example, "getting the job your PhD advisor has/had". There just aren't enough of those to go around, if nothing else. Can you clarify?

If you want to get your "teaching career" going, I wouldn't worry a lot about the ranking of the school. Teaching focused positions are going to look at your teaching experience, if you don't have a lot of this then you may need to take a visiting position to get some experience.
A school ranked lower than the one where you got your PhD might view the name of your school as something they want associated with your department. Or they may assume you are more interested in research and not with teaching. These positions can still be incredibly competitive, but if you have the right background you may have a chance to be hired right out of your PhD.
If the school you attended was highly ranked, then schools ranked the same or higher are going to want most of their tenure track faculty to be focused on research, or at least to have a good research program to get undergraduates involved in. If you don't have a stellar publication record, you will absolutely need a postdoc. Even then, you will need a good postdoc with good research results. Even then, it will be very competitive.


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Can I deviate from my fellowship proposal?
I got the prestigious Marie Curie individual fellowship (postdoc). The proposal I wrote is good but I'm not sure if I want to stick to it 100%. What is the expectation for such fellowships in this regard?

The question about expectations should be answered by the organization behind the fellowship, rather than by strangers on the internet.

I agree with the comment of @DavidKetcheson and I'd add that the answer will probably depend on how far you want to deviate from the proposal. I can easily imagine the grant agency being much happier with a deviation from set theory to model theory than with a deviation from set theory to differential equations (or to biology, or to archaeology).

What did the grant officer say when you called them?

@henning: I will let you know when I do. The grant is still to start in a few months, so it's quite early to ask formally now.

I cannot answer for your case, but my experience is that fellowships are generally pretty flexible in terms of what you actually do while on them.

As stated in the comments, it depends of the funding institution (and of the (researchers of the) institution which received you, if distinct from the former), but in my experience, nobody is checking that a research proposal is followed to the letter (at any level), and even less so at the post doctoral level.
The writing of the research proposal itself is an exercise to show that you are able to propose your own research topics: if you were awarded a post doctoral fellowship, you passed the test. Your next step is to publish on you own or at least without your PhD advisor, so that you show independence: it does not really matter if you follow your research proposal (albeit if you wrote it well, that should be the easiest way!) or not, and you should definitely take advantage of any opportunities that you find on your way: you are in the business of doing research, not of implementing research programs!

US-based research administrator here.  What you are referring to is called "change in scope".  This applies to any type of research agreement. When you want to change the scope, it can invalidate your agreement.  In all cases, fellowship or funded grant or contract, you need to go back to the terms of your agreement and see what is required in the case of a change in scope.  Often it requires prior approval from the sponsor.
In US Federal grants, this case is written into all of the terms of agreement, and is often tied to budget items as well.  E.g., you may be able to rebudget your funds to buy an unexpected piece of equipment, but does it change the scope?  If you had a machine and it failed and you need to replace it, that is different than you suddenly deciding to buy equipment to follow a new line of research that you hadn't proposed.  The approval in this case isn't so much for the rebudget as it is for the change in scope.
While I cannot comment on exactly what your agreement says, I can say unequivocally that the only answer to your question is to examine the contract.  Even in cases where sponsors routinely give out the same award, technically every fellowship, grant, or contract is unique.  They may refer you back to common terms, but your institution's policies may also dictate additional rules that you must follow.  At my institution, I would be looking to see if you are actually working on your PI's science instead and then would need to discuss whether or not that is ok.  The NIH fellowships are very particular about such things, and the NSF fellowships are more silent.  This type of analysis isn't always written into an agreement, but it is a basic principle of allocation methodology in research administration.  Find your local research administrator for help--they should be trained to do this work on your behalf.  If you do not know who that is, check in at your department's staff and ask them.  They should be responsible for helping you understand what is allowable, allocable, and reasonable.
As for someone checking in on you -- you have to report back in your progress reports.  What I always say to my PIs when they ask questions like this -- Will you be embarrassed to report this in your progress report without having checked in first?

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Is mathematics becoming more applied, while engineering is becoming more math-y?
Is mathematics becoming more applied, while engineering is becoming more math-y?
I’m seeing curriculum changes at our school that has pretty good math, physics, and engineering departments, and it seems that the vision is that soon math majors will be taking courses in both the mathematics and engineering departments.  And likewise, it seems that some engineering research will be done collaboratively with mathematicians.  So I'm wondering if this is a general trend that is happening, at least for universities in the U.S.
Are there any references that support or oppose this trend? For example:

Bibliometric studies.
Publications of professional societies.
Official statements by departments, faculties, or universities.

In that case: edited. (CC@FedericoPoloni).

Engineering's definitely getting more math-y, as are STEM fields in general.  The kind of math that we commonly do today would've been grossly impractical before modern computers, but now we can basically do a system of coupled PDE's where someone 100 years ago would've used an estimate from a linear correlation.  A lot of this stuff, e.g. CAD use, seems fairly obvious, though; dunno precisely what to comment on in an answer.

@D.Hutchinson Anecdote: I know of a university with separate math and applied math departments where the departments are having turf wars about teaching calculus and linear algebra, especially to engineering students. When the number of undergrads a department teaches improves its funding, TA support, and university-level clout, there is probably lots of pressure for (pure) math departments to cater to engineering students. Many schools may be renegotiating these questions between departments (e.g. math department teaches all calculus, applied math teaches all stats) leading to what you see.

@Nat what's CAD? ... is it like MatLab?

Just because you see a change from an emphasis on topic A to more emphasizing topic B does not imply that this is a trend a department wants to continue. So I think a statement such as "it seems that the vision is that soon math majors will be taking courses in both the mathematics and engineering departments" is not valid. If the vision had been that that should soon happen, then that would have been reflected in the state of the curriculum, not in the direction of motion of the curriculum.

The Wikipedia page for computer-aided design (CAD) seems a bit overly focused on basic 3D mechanical design CAD, but that's basically it.  The gist is that just about anything we engineer today, from computers to planes to cars to chemical plants, etc., can be modeled up on a specialized computer program, much like traditional engineers may've drawn up blue prints.  But since they're automated, the computers can use highly complex, esoteric models for every little thing.

This has made the Engineering world a different place.  Now, field-level engineers can just use software often without knowing all of the details behind how it works, while research-/design- level engineers have to have tons of technical knowledge to build tools.  There's little call for anyone to not use advanced math; field engineers just have the software do it while researchers need to advanced math/logic to make continuously more advanced tools.

@WolfgangBangerth hmm ... it's actually the direction of motion of the curriculum, I think  One of the math profs. is in charge of this transition and even said that that's the vision to aim for, according to a departmental update.  By the way, we've used some of your work - thanks, dawg

@D.Hutchinson -- what I meant to say is that just because you have a direction does not imply that you have momentum to keep going in this direction.

I'd say engineering has always been an applied mathematics field, just with added labs and domain knowledge. The mix varies depending on how broad the dept tries to be, i.e., targeting specific jobs/niches (more domain knowledge) versus training more generally for everything at once (more general methods, i.e. math).

I agree with all the comments, that engineering has become more mathematical because of the rise in computers and similarly applied mathematics is growing because of its well poised position to aid in many developing engineering problems.
While I could not find any studies to back this up, I made some graphs of my own from the Web of Science.  The first two compare Applied Math
 and Pure Math.  The volatility of the Pure Math graph leads to a difficult comparison.  Similarly, I took screenshots of Engineering with Math and All Engineering. Which I believe more clearly shows a growth in engineering-math publication in the past five years or so, as compared to all engineering whose rise began earlier.
Similarly I looked into Arxiv, as it supplies a nice single source of data.  These results are for number of uploads/publications.
------------------ 2000-2004 ---- 2014-2017 ---- Growth
App. Math --------- 577 ------------- 5456 ------- 9.45
Pure Math --------- 253 ------------- 1399 ------- 5.52
Eng. Math --------- 14 --------------- 476 ----------34
All Math ---------- 14,408 --------- 102,382 ------ 7.1
While these figures are based on publications, and therefore could be caused by changes in publishing practice I think they do give decent, data-driven evidence for the claim that math is becoming more applied and engineering is becoming more mathematical.

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How to contact author whose work I built off of?
I'm working on a manuscript for a scientific paper that alters/adds onto what another study found over 20 years ago. This old study is highly cited and the graphs are reproduced in a lot of textbooks, but my study says that the old data isn't the whole story. In part of my manuscript I compare my data to that in the old study and I'd like to include a graphical comparison. How do I contact the authors of the old study and ask for permission to reproduce their graph? Do I tell them about my study? Do I let them read my manuscript?

It's appropriate to contact the authors, particularly if they are still active researchers. Before sending them the manuscript or telling the entire story, perhaps ask if they would be willing to keep your results confidential until publication.
As far as formal permission to reproduce their graph, it's very likely that the authors don't hold the copyright. Most authors sign over copyright to the publisher, so you may have to query them. Even so, it would be polite and advisable to notify the authors that you are planning to reproduce a piece of their work for comparison.

As for the graph - the issue of copyright depends on whether the poster wants to directly reproduce their graph or use their data for a comparison. The second alternative is easier from a copyright perspective, and (probably) only depends on if the original authors wants to share their data.


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How should the order of faculty names be in a letter?
Assume we have 6 professors with names that are:

Professor: David Goh and Gautam George
  Associate Professor: Juan James and Laura Alfaro
  Assistant Professor: Abigail Baldwin and Jose Choudhury

How should the names of faculty members be ordered in an application or cover letter (previous or potential collaborators)? First order by rank and then order by last name? Or just by last name.

George, Goh, James, Alfaro, Baldwin, and Choudhury
Alfaro, Baldwin, Choudhury, George, Goh, and James

Can you use names that are easier to memorise, please, e.g., Alice Bobson, Carl Doe, … Also, what kind of letter are we talking about?

Any letter, including cover letter

Wait, why have you ordered the names as you have?  I will assume the second "Associate" is a typo for "Assistant", but why aren't you introducing your arbitrary variables in alphabetical order within the ranks?!?

"Any letter, including cover letter" That's not a very helpful response.  There is no preferred ordering independent of the context.  If you're writing an introductory paragraph in a letter to your parents in which you're going to talk about your professors, order them in the order you talk about them (if you like).  Or if you're writing to your friends, order them in a way which seems most amusing (if you like): Cox, Zucker rather than Zucker, Cox.

The 3rd is assistant.

A little bit of context (i.e. the sentence before and after this list of names) would help clarify this question, since right now I'm having trouble understanding why anyone would ever need to include a list of six names in a letter...

@ff524: At least in my field, when people apply for a faculty position, they (more) often (than not) include in their cover letter a list of names of faculty that they think would be interested in their work.  Among other things, this is an indication that the application should be showed to these faculty members even if they are not on the relevant admissions committee (which is often a small subset of the faculty).

@ff524 Can be an application letter or cover letter

@Wrzlprmft I have added more details

FYI, your 1. is not a correct instance of ordering first by rank and then by last name.  (I don't see why you need examples at all, but your presentation of them certainly makes your question more confusing.)

Better, but you really shouldn’t use the names of actual persons.

@Wrzlprmft they are random names

@Pete L. Clark I have seen # 1. been used so, I wonder if its right or 2. is better

@Thomas: This is getting a bit tedious.  #1 is not a correct instance of what you said in your question was the first option: namely, ordering first by rank and then by last name.  As to which of 1 and 2 is actually better: I already answered that question.

@ThomasLee: I may underestimate the chances, but they do not appear that random.

You should also clarify why previous collaborators and potential collaborators appear on the same list?

From the choice of tags, I think you mean a cover letter for a job application.  In that context, I suppose you are giving names of faculty that you want to work with and/or have a research connection with.  (It couldn't hurt to make this more explicit in your question.)  Assuming that:
Ordering the faculty by rank doesn't make a lot of sense to me.  Alphabetical order is perfectly reasonable and common.  However, the more names you list without any commentary, the more it looks like you are fishing for something rather than calling attention to a real kinship or commonality.  Thus, if you are going to list more than one or two names, it would be a better idea to break it up into several sentences and describe what you think the intellectual relations are.  If you do this, you should group by the type of connection you have -- e.g. "Professors X and Y may be interested in my work on perverse sheaves" -- and it would make sense to list the strongest connections first.
If you just give a list of names out of alphabetical order, it's not clear what the ordering means.  (In fact you do so in the statement of the question: within the faculty ranks, your variable names are not alphabetized.  Once I noticed that, I found it very confusing.  That may be useful information for you.)  Two possibilities: (i) The names are listed in descending order of importance (somehow).  (ii) You wrote the letter very quickly and just grabbed names in the order you found them.  Nobody likes to be ranked down in importance without understanding why, so I wouldn't do this.
Added: The above answer is from the perspective of an American university.  Maybe academic rank is taken more seriously as a form of social rank in other cultures (it really isn't in the US: a new hire assistant professor calls all her colleagues by their first names immediately on arrival).  If by chance you are applying to a university in Asia, then I wouldn't assume this answer applies.

I'll say it more plainly: Ordering the faculty by rank doesn't make sense (at least in the US).

In parts of Europe (at least the Nordic countries) it also makes no sense. As Pete mentions, in the US newly hired assistant professors are on a first name basis with the other faculty. Here, you are generally on a first name basis with any faculty you speak with on a regular basis, even as a first-year student. (It took me a while to get used to the PhD students calling the faculty "professor lastname" or "doctor lastname" while I was at UGA).


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Should a phd student try present a review paper in a conference?
I am a PhD student and I am doing a literature review for my dissertation. Despite the fact that I am working on the topic, I am not an expert; but I have enough information because of my previous research during MSc and have a published paper in my CV.
I submitted an abstract of the review paper for oral presentation in the most important conference about the topic and it was accepted. The review is about the developments and contributions from researches in some countries. Then, I told my advisor and he suggested me not to submit the final paper because he thinks that kind of review paper must be done by experts, not by PhD students (it would seem such as teaching to experts); further he thinks I will spend a lot of time which should be spent on my PhD dissertation. I really want to present the review; however, I do not want to contradict my advisor.
Do you think it is convenient to present the paper?

"I really want to present the review" Your advisor told you why it might not be a good idea to present the review at your career stage.  I find his reasoning rather convincing: note in particular that he is not bossing you around, but looking out for your interests and explaining why.  What is your reasoning for wanting to do it?

Also: I read your last question and thought "Gosh, this is a very difficult set of constraints.  In my field it is very rare to nonexistent for someone to give a talk in this situation, and I honestly wouldn't have the faintest idea what to tell the OP.  In fact, maybe it would be better not to give the talk."  And now this question.  Your advisor sounds like he knows what he's doing...

In addition, is there anyone else in your research circle who thinks it would be a good idea, and why?  There are both positive and negative aspects to it, and it is easy to spend a lot of time on such a presentation and have it hamper your current research.  Is it possible the organizers misunderstood and think that you will be presenting a new idea?  If you really want to go ahead on this : a) get some help, a colleague or conference organizer who approves of your presenting a review, and b) convince your advisor by working hard that it won't negatively impact your research.

In principle I agree with @PeteL.Clark, but it is certainly weird that the advisor agreed to hand in the proposal for the presentation, and now wants you to step back after acceptance. This sort of behavior is not good for the morale in your team.

@xLeitix: I don't know how things are done with these sorts of conferences, but it's not at all clear to me that "the advisor agreed to hand in the proposal for the presentation".  From what the OP wrote it sounds to me like the OP initially submitted the abstract without the advisor's knowledge.

I am of the opinion that even young researchers can have something to contribute in a review form. After all, the more senior folks rarely read all the recent papers in the detail someone new might, and more often than not, the devil is in the details. From how it was put in the question, it sounded like the main objection the advisor has is your age. You should find out if this was just a nice way of saying that he believes that it is in fact the content of your paper that is lacking.

Your supervisor is right in saying that review papers should be written by experts, not PhD students.  Review papers are often read by other PhD students who often uncritically assume they are authoritative and reliable, which is unlikely to be the case if written by a PhD student (who will not have the experience to correctly judge the value of different approaches).  PhD students need to produce a review of the area in which they work for their thesis, but this is to show that they have acquainted themselves with existing work, it doesn't mean they are in a position to evaluate it, ... yet.

Actually, the weirdest thing to me is that a literature review for a PhD thesis would be even considered for a talk. Are you really going to stand up in front of the experienced folks in your field and critique the state of the field? This could either quickly make you a superstar, or, much more likely, make it obvious to many that you don't know as much as you think you do. Remember - those folks are likely to be involved with hiring you as a post-doc or staff scientist in a few years...

What is the contribution of your paper going to be? It's a review paper, so you're not going to be contributing any new techniques or results. You say you're not an expert so you're not likely to make any deep connections between different areas of the field or between your field and other areas of study or present any great insights about why things are the way they are, where the field has come from, how it got where it is and where it's going to go next. So what is the contribution?
The usual deal with a conference talk is that you stand up and tell people about your work. They want to listen because they don't know about what you did and you understand it better than they do. What you're proposing is that you stand up and tell them about their work. They already know what they did and you're not an expert on it, so they (at least, some of them) understand it better than you. Why do they want to listen?
Your main motivation seems to be to score "CV points" by presenting at the conference. That could easily be nullified by giving yourself a reputation for not doing research and giving superficial talks with nothing much new in them.

"Your main motivation seems to be to score 'CV points' by presenting at the conference." Maybe.  I would really like to know the OP's motivation: I asked about this shortly after he asked the question.  I am a bit disappointed that he has already accepted an answer without responding to this question.

There are two points that can be considered on your question.

You work under supervision of your advisor and he is, to some extent, responsible for your progress at this stage of your academic life. If he thinks that the review paper should not be sent by you, it means that based on his assessments, you are not prepared enough for submitting a review paper.
Moreover, it is normal that a PhD student think that he knows many things about his field of research; but his advisor has done many more researches and advised many more students and I think that his assessment about your knowledge is far more precise than your assessment about yourself.
As an example, the PhD student is like a driver in a car and the professor is like someone in a helicopter, you have driven far distance in your car and feel like you are far away from the other drivers. But the one in the helicopter sees the way, you and other drivers better from the top and he knows that you are still in the middle of the way, not more progressed than the others.
As a comment on the type of your paper, you are trying to write a review paper which I also think, as your professor had mentioned, is normal to be published  by some experts in the field. I mean the ones who have worked years in industry, or the professors who have released many publications and supervised few research students. I think that review papers should be written and presented by a more aged person than a research student.
Imagine the conference that people aging fifty and sixty are sitting in audience and a 26 or 27 years old PhD student goes and reviews the topic for those experts. I am not saying that the PhD candidate does not know anything about his field of research; but he needs some more years of research and work to become an expert in his research area. It is a little odd to see a PhD student publish a review paper, as he is not an expert in his field.

But a PhD student may participate in a review paper which is going to be published in a journal or presented by a professor. But still there needs an expert or a professor to be the main presenter and contributer of the publication.
However, the situation may be a little different from one major to another; and from a research group to another. I think that your supervisor is the best guide for you.

not all phd students are 26 or 27, some could have worked in the industry for years to later enter into a phd program

I talked as general, and you are right. Some of them come from industry and are older than that norm.

@user1938107 Or far younger than 26 or 27.

Is it normal to be far younger? I have never seen a PhD student younger than these ages. Because the bachelors and masters usually take 4 + 2 years and if the person enters the PhD on time, he is about 25 years old.

@Parsa: In the US it is very common to begin a PhD program without a master's degree.  I spent (nearly) five years in a PhD program and received the degree at the age of 26.  For Americans in STEM fields, this is quite common.

"Imagine the conference that people aging fifty and sixty are sitting in audience and a 26 or 27 years old PhD student goes and reviews the topic for those experts" I'd think "They clearly have more time to read the literature than I do."

Addressing a few points in your question individually:

The review is about the developments and contributions from researches
  in some countries. Then, I told my advisor and he suggested me not to
  submit the final paper because he thinks that kind of review paper
  must be done by experts, not by PhD students (it would seem such as
  teaching to experts)

I strongly disagree with your advisor. Non-systematic reviews are often done by senior people because the papers can get a bit opinionated, touching on what needs to be done, the quality of evidence, etc. in a subjective way, and it's nice to have that come from an authority figure, someone whose seen the field develop, has a broader view, etc.
But that's not to say that all review papers are like that, or that you're not an expert. My most cited, and one of my best received papers, is a review I wrote as an undergrad.
Additionally, unless your abstract was incredibly obfuscated, it's been accepted - which means the reviewers and conference organizers thought it was worth doing. It seems like the relevant determination as to whether or not this abstract "belongs" has already been made.

further he thinks I will spend a lot of time which should be spent on
  my PhD dissertation.

This is a more valid concern, imo, and you should work hard on making sure that this doesn't obstruct your PhD research. Ideally,  lengthy review paper would be about the topic of your PhD, and thus do double duty.

I really want to present the review; however, I do not want to
  contradict my advisor.

This is a decision you have to make - how much do you want to present the review, how much you're willing to risk irritating your advisor, who is paying for you to go, etc.


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Has any research been done into the marks awarded by markers using different strategies for exams with multiple questions?
I think anyone who's ever marked an exam with more than one question or section has wondered how to attack it, i.e.
Option 1) Mark all of the questions in a single script before moving onto the next student's script; or
Option 2) Go through each script and mark all the question 1s, then 2, and so on.
Idle rumination gives good points and bad points for each side (and possibly some more approaches), for example 1) means you can get through it faster since you're in 'question 1 mode', then 'question 2 mode', ... (i.e. less time spent switching your brain between different topics), and is likely more fair in the sense that you can consider all the answers to question i at the same time and make sure marks are distributed in a reasonable fashion.
On the other hand, marking a script at a time means you get used to a student's hand-writing, and may perhaps give benefit-of-the-doubt (or not) depending on the understanding that the student has shown previously in the same paper (but hopefully not on the basis of external bias from knowing anything about the student aside from what is on the script).
It would be interesting to see whether one technique or another tends to correlate with higher marks given by a marker, or fewer challenges once the student receives the script back (in cases where that happens), for example.
A brief search via scopus, arXiv and popular general-purpose search engines does not turn up anything particularly relevant. Does anyone here know of any published research on the matter?

Related: http://academia.stackexchange.com/questions/3211/what-are-some-ways-to-increase-grading-speed

As a second-hand anecdote, I believe in the book Thinking Fast and Slow by Daniel Kahneman, he recounts trying to grade one problem at a time vs one student at a time (maybe even multiple assignments of each student at once). What he found was, even when he tried not to, he tended to somehow mark a student more consistently with early work when he did one student at a time. There is a consistancy bias - if a student proves themself smart and capable early in the course, one will naturally grade them higher later because we'll assume they understood even if they didn't explain clearly...

...but when grading one assignment - or problem - at a time, he found that while his grading was objectively more fair, scores any given student received were much less consistent - sometimes good students did terrible work! By grading one question at a time, it makes it very hard for one - intentionally or accidentally - to favor or disfavor any student. This was not a study, but it's from a smart guy, you see, so sub-rational psychological bias insists you should believe my second-hand anecdote even if it's just an anecdote and argument from authority. :)

The question we'd like to answer is:
How should exams with multiple essay-type questions be graded?
Note: It isn't possible to answer this definitively due to unavoidably large variations in grader performance and exam structure.
Each course topic, exam, and set of graders is going to be unique and your own experience and judgment are likely to yield better results than any prescription of what you should do.
This is a large part of the reason both teaching and assessment are challenging and generally require a lot of experience, testing, and adjustment to get right.

While you're own views should necessarily be more relevant for your teaching situation, here is One perspective to consider:
Ideally, we strive to consistently award the same amount of points for the same level of written work. However, it is likely that our impressions about the abilities of individual students (from lectures, office hours, etc.) actually have a negative impact on the overall consistency and reproducibility of the results when marking written exams.
Whitfield et al. compared oral evaluation results to written exam grades and found that facilitator impressions of student knowledge overestimate the performance of the weakest students.

[Whitfield, 2002] "... there is little overlap in the factors which
  account for facilitator’s ratings and actual student performance ...
  Our conclusion from these studies is that an assessment given by a
  facilitator for student knowledge is not likely to be useful."

The take-away is:
knowing which student's answer you are grading will make the range of scores wider (lower reliability) for the same work, and generally lead to higher grades overall.
For the most reliable grading:
the grader should mark answers non-consecutively and randomly from the entire set of exams in order to:

reduce the chance of identifying the student that provided the answer
reduce the comparative influence from one marked answer to the next

The following articles look promising for further investigation, however, I don't have convenient access to these texts, so they are offered without comment:
"The interrelations of features of questions, mark schemes and examinee responses and their impact upon marker agreement," Assessment in Education: Principles, Policy & Practice, Volume 18, Issue 3, 2011.
"Towards a model of the judgement processes involved in examination marking," Oxford Review of Education, Volume 36, Issue 1, 2010.
"Double-marking students' work," Assessment & Evaluation in Higher Education, Volume 19, Issue 1, 1994.
"Responses to written work," Educational Review, Volume 30, Issue 2, 1978.
"A further study of the reliability of English essays," British Journal of Statistical Psychology, Volume 7, Issue 2, pages 65–74, November 1954.

Related SE questions:
double marking
---- keeping focused
---- avoiding bias


text:
Where do new ideas come from in research?
I am a PhD student working in computational theory which includes some amount of mathematics. I am currently in the middle stage. I have read a few research papers and have been working on some problem. I wonder how new idea's come into mind? I spend most of my time thinking about research problems again re-reading the problem statement and many times I try to read related works. I have been able to come up with new ideas but not something which is something like whole research paper. I try to focus as much as possible but still not focus is not working. I am wondering how researchers come up with new idea.
Question: How to to come up with new ideas or approach for research as a PhD student?
Some advice I have got is read-read and write and repeat but does not work for me.

Read, assimilate then imagine...

Sometimes real world problems are an inspiration for research problems.

@Pont: If I understand correctly, that question is about coming up with interesting and feasible problems, while this one is about coming up with ideas on how to solve a given problem. @ abaa: If I am correct, can you please [edit] your question to clarify this?

Wait for the answer to find you. Usually at the most inconvenient time.

@abaa Why are you not referencing to failed experiments and non-working computercode as inspirational source for new ideas? Do you believe that this unscientific?

You have an advisor, right?  Ask them.  They know you and how you think.  We don't.  2) Even at a top mathematics department, a not inconsiderable fraction of graduate students find out that they're not so good at research in the sense that they can't come up with good ideas for the problems they work on; they end up doing something other than research after their PhD.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
How to speed up my research as a PhD student?
I am currently in a problem-solving mode of my PhD (theory, middle stage). I am working on something which is incremental on the last half a year. Until now, I did not get anything non-trivial. I noticed that I have become slow as compared to the initial two years of my PhD.
I am wondering how to speed up my research. I have tried to scan as many papers as possible. I am also trying to discuss my research problem with other students, but it is not working.
Question: Is it natural that you become slower in the middle of your PhD? How can I speed up my research?

It takes time to mature and for the material to take root in your brain. Sometimes the fruits / insights of your studies come directly, but sometimes knocking anytime 1-10 years after you first took the course / read the book / had that talk with a professor is not too uncommon. It is a natural process. The only thing you can do is to keep at it and not give up.

Sidetracked discussion about paper-output statistics moved to chat. Please continue discussing there.

Tweet from a PhD procrastination expert (literally his career). You are in the bottom part of that rollercoaster, as most PhD students have been, are and will be.

In many disciplines, research amounts to an endless series of frustrations, setbacks, and failures punctuated by the occasional success. So while it’s not necessarily true that there’s a slowdown in the middle, it’s usually the case that there are periods of lower output than others.
The only projects that go smoothly from start to finish have either been extremely lucky or tend to be development-based or “following on” from someone else’s struggles. So don’t be discouraged by having a period of relatively fallow productivity—it happens to nearly everybody. It’s a sign that your problem is non-trivial and requires some work and effort to complete successfully.
As for “speeding it up,” I don’t think that’s really possible to do by scanning papers instead of reading them. It’s hard to make specific suggestions without knowing more about the discipline, but productivity in research can be elusive at times, and there really isn’t anything you can do to accelerate things, unless you can somehow try more different solutions during your workday. So perhaps it’s a matter of having fewer distractions and disruptions to your day.

Are there actually disciplines where research is not an endless series of frustrations, setbacks, and failures punctuated by the occasional success??!

@JeffE  Yes, generally very new disciplines, which are often ignorant that they are repeating the same things that were done in another field 50+ years ago.

As some one who's done their PhD in 3 years and supervised PhDs for 10 years:
1 - Cut the crap
It's easy to get overwhelmed with all the papers you read, so many topics, so many possible directions to go in. You need to focus. I suggest you start your first paper right now. From all the research you've done, what's a small topic where you can contribute? Make a list. Pick one. Start there, decide what more research you need to do to turn it into a paper, do the research, write it, publish it.
I did exactly that after 1.5 years in, and really just by fluke. My paper wasn't even exactly aligned with my main research topic, yet it did wonders in getting me going.
2 - Chunk it
Once you've written you first paper, keep going. If you get to five published papers, you're done. And the great thing is that you have your PhD already mostly written, just fill in the gaps, thesis done. It may seem counter-intuitive as you want to answer the one big question, but trust me, once you chunk your big research into little topics it becomes a lot easier. And you figure it out along the path. Build on each paper, you'll get there. You may even not get to answering your original research question fully, but with 5 published papers, who's going to argue with you?
3 - Ask the experts
Talk to other lecturers, researchers even outside your faculty where it fits. I had some problems I couldn't solve. Turned out most of the experts in the field at my uni hadn't got a clue either. Until I found one who did. Go to conferences, make connections. That'll also help you in your post-PhD life. Don't give up, someone out there will be able to contribute something quite valuable.
4 - "Trust your instincts, Luke"
The brain works in mysterious ways. I chewed on a problem for a year. Then, one morning in the shower I solved it. This is just how it happens. There's a physicist who said, "men who don't shave can't solve problems." Times of self-reflection, that's when the ideas come. If you could figure the answer out in a day, they wouldn't give you three years. And I'd fit in exercise, even just go for an half an hour walk. Lot's of famous people got their ideas that way.
5 - Get off the hamster wheel
I certainly didn't work hard, yet did it in 3 years. There are students working 14h days including weekends. And they take 5 years. Why? Because all their doing is running on a hamster wheel. I had a student who went kite surfing every afternoon and did a great thesis in 3 years. Why? Because he focused on the finish line. It's easy to get caught on the hamster wheel. You're doing work, right? I spent 14h in the lab today, well done, pat on the shoulder. The reality is, if it's not helping you to get to the finish, you're wasting your time. Stop self-gratifying work, do the hard stuff, the work that'll actually get you there. And if you don't know what that is, figure it out. Yes, it's harder than just sitting down and doing "something". Take a snapshot of what you know and make a plan from that. Keep checking your progress against the plan and modify the plan as necessary. And that links back right to point 2 - if you keep pushing out papers, you're making measurable progress. Plus, you build confidence that what you're doing is actually valuable. You got proof in print.

I really love this advice, especially "cut the crap" and "Get off the hamster wheel". I think I needed to hear this to complete my thesis.

I would see a PhD studenship as a superimposition of two effects.

The more you know and the "deeper" you are in your topic, the more efficient are you.
The more projects, topics, contacts (!) you have, the slower you are.

The second issue is present in every job I experienced, my experience being limited to programming or academic jobs.

Why the preface? Well, in your second year you get faster due to (1), but the back-flow of (2) makes a significant appearance. Producing a revision for a previously submitted paper, fleshing out a small improvement in old code, understanding old code or someone's else research – all these required issues slow you back, sort of. Of course, then you can boast another well-placed paper, know more about some further methods, etc. But at the moment it's perceived as a slow-down.
In your first PhD year you can just start reading / programming / whatever and it feels super productive, even if the actual research output is lower.

tl;dr: Don't fret, it's a phase.

I left a PhD due to some extreme life hardship (unrelated to the PhD), things I wouldn't wish on anybody, but going through really rough times taught me a lot about how to be successful. Here is my advice.
1. Personal development is not wasted time
It may be tempting to spend all your time on your PhD. But even seemingly frivolous pursuits like learning to speak a new language or learning neuroanatomy will help you find new solutions to problems. Read books or take cheap online courses related to productivity, creativity and success.
2. Learn to think for yourself
It's easy to run a test and discover a 30% speedup for a 10% cost. It's a lot harder to answer the question "What proportion of businesses will be willing to spend money to implement this?" or "How come nobody is using my tool?" To answer these questions you need to be able to put yourself in someone else's position; you need to think about the dynamics of the communities and publication channels through which people learn about new things, and you need to try to reverse-engineer the success of other ideas. For instance, I discovered a powerful library for my research because a student had written a series of "novice-friendly" blog posts about how to use the library his group had developed, and this came up in Google while I was searching for something else.
This kind of thinking will help you pursue ideas with more value and impact. I have found that as I got better at this, I became much better at coming up with clever ideas, too.
3. Go back to first principles
There are a lot of implicit assumptions and habits of thought in the way we currently do things. But often they are imperfect. They are useful tools, good approximations, but don't take them as gospel. Explore the unwritten assumptions. Get your head around why these tools are effective, but also try to understand their weaknesses and limitations. Think about problems from first principles, without the support structure. Think about the problem in a "boots on the ground" perspective instead of an "ivory tower" perspective. (Depending on your field that may be rushed doctors needing to make "least risk" judgments, or the crazy compiler and CPU optimisations and cache hierarchies that make actual computer performance quite different from the theoretical.) Two perfect examples are asymmetric cryptography and cryptocurrencies, which both solved a theoretically impossible problem by using solutions that are theoretically breakable but are good enough in practice.
4. Valuable habits often sound crazy
I found that simply writing a weekly report on my work was often a surprising source of new ideas. But doing unusual things can often spark even more creativity. Here are some ideas:

Hand-write or dictate a report instead of typing it.
Write a "letter your grandmother could understand" once a week about your work.
Write an article once a month blasting your work (i.e. tearing it to pieces) or religiously defending the current state of the art.
Write a list of the problems you are currently stuck on. Create a visual representation of the problem. Now simplify each problem to a "logo". Choose a piece of music that represents how you feel about each problem. Listen to them in random order.
Do a similar thing for existing solutions to related problems.
Use the Po technique to explore solutions to each problem. Try other de Bono techniques.
Record yourself giving a short (or long) overview of your research topic. Play it back and hopefully things you missed will jump out at you.
Create mind maps of problems and solutions. Try finger-painting them.
Read out a description of your research area but replace one of the key words with a random word like "pigeon".
Don't forget there are lots of apps that may be able to help.

Different things work for different people, of course. But try to stick with something for a while, as it is very easy to lose a good habit.

I can't say that your experience seems any different from what I went through in my PhD. Completing a PhD is intentionally difficult. There is no magic formula to completing a PhD, no quick fix or way of speeding up the process- in fact, if anything, panicking and trying to find fixes could potentially have an adverse effect on your mindset impacting how quickly you complete your research.
Scanning papers will definitely not help you. However, pick a particular thread and pull it. What I mean by that is thoroughly read a couple of papers and then work back through the references they provide- really deeply get involved in one particular aspect and see how that relates to everything you do. There is no point using a scattergun approach- the only effect that will have is to make you think you're making progress when really you're probably not!

Focus. A common mistake that PhD students make, and in fact many humans make throughout any career, is that they don't focus when the time is right. In the beginning, it is very good to venture into everything and anything to see what's out there and what is worth doing. After the first year it is about time to draw a very specific goal with a sharp plan that will get you there even in the most pessimistic scenario. That's where your PhD adviser comes in most handy as they know this game very well. Do not disperse your attention on anything else until you get that PhD diploma. At some point closer to your 3rd year you may (and should) find out that there are better things to research or do in general. Good, make a quick note of it and put it away until you are done with your PhD. Until then, follow the plan and get it done.

I think it is like an engineering design procedure. From basic to detail design stage you need more time to pass from idea to reality. Maybe the amount of spent time to achieving goals is greater, but the quality and type of achievements is crucially different.
But if you still think should be more productive, do a research on “how to increase your productivity“ and so on to find out very useful online papers and weblogs about this.


text:
How to deal with the (hidden) feedback from research supervisor?
I am a PhD student who works in a theoretical field. Often times, I have discussions with my research supervisor, and I try to do as much work as possible.
It seems that many times these days my research supervisor does not seem happy with my performance. He often doesn't seem satisfied, judging from his facial expression and body language, but when I ask him, he just says ok. Most times, I leave the meetings in illusion (meeting was good or bad). I feel pressure due to these things. I don't know if it is a common thing in academia or not.
Question:    How to deal with the (hidden) feedback from a research supervisor? This is constantly creating pressure on me.
There is a difference between feedback and hidden feedback. For explicit feedback, we can ask the supervisor once in a month or something like this.

Do you have meetings in English? Could he have a hard time understanding you and therefore look puzzled from time to time?

@Mark  In english and in native language also

Just to make sure: are you sure there is no culturally triggered misunderstanding behind this?

Is there a gender difference that may hide a difference in generic conversation style, which is now becoming apparent. Maybe not quite 'on message' but there are suggestions that some of that could be ancestral https://cup.linguistlist.org/academic-books/discourse-analysis/duels-and-duets-why-men-and-women-talk-so-differently/

Nitpick, but I don't think that something so obvious should be called "hidden". Perhaps "unspoken" or "implicit" would be a better word?

Maybe it's not unspoken criticism but just the personality of the supervisor. He may be expecting you to perform as you do, and when you do, it's only a confirmation of his expectation, not something to get excited about to him. So your work is "ok" for him with you meeting his expectations. Whether those expectations are relatively high or low with regard to other researchers in the field does not make a difference for his reaction.

Actively ask for actual feedback.
Of course, you need to be prepared for any feedback that is to come.
I am reading things into this... but I suspect you are reading feedback into their facial expressions and body language because you in fact need more feedback.

@abaa you can never know its because of you. Maybe your supervisor is under stress for unrelated stuff and gets the "upset face" easier due to  this, gets tired easier, etc. The only real feedback is actual feedback, any interpretation of gestures can be  just because you are too worried

@abaa Once every one/two months should be sufficient to gauge whether this is an actual issue versus just a supervisor quirk. If he's been making faces publicly for the past month or two, but in private 1-on-1 meetings says you're doing fine, either his facial expressions don't correlate with his opinion (in which case just ignore the expressions in the future), or you have an extremely passive-agressive supervisor. -- (Also, if you feel like once every two months is too infrequent, you should actively approach your supervisor about possibly increasing that frequency.)

Also be specific when asking for actual feedback, e.g. "I'm struggling with the approach I am using for this problem, could you take a look and perhaps suggest some alternative methods?" or "I wrote this piece for my paper but it doesn't sound right, could you give me some pointers on how to improve it?". This gives your supervisor some idea of what you want to know. If you want to discuss your whole project then plan a meeting and go over all the details one by one.

I had a supervisor who made an "upset face" whenever I talked to her, especially during my presentations.
I told her my observation and asked her (politely) whether my interpretation was correct.

You seem to be frowning. Is it because you disagree with what I am saying?

It turned out her "upset face" was actually her "focused face".

No, I'm just a little tired and trying to concentrate. Please go on, it's interesting.

In short, "hidden feedback" is an interpretation you're making. It may or may not be true, so make sure to double-check.

A somewhat similar experience I have: some dialects in my native tongue often sound rather accusing or complaining to me. Turns out the "normal melody" in those dialects is rather close to the intonation the dialects I'm "at home with" uses to mark accusations and complaints...

This. Even if I have a disapproving frown, it may be aimed at the math problem that refuses to be solved, not at whoever happens to be there ;-)

I agree with henning. My students think I am mad at them, but I'm actually concentrating on what they are saying. Try to find out what "ok" means, and what the Profs expectations are.

A short supplement to skymningen's answer: I have made the experience that when actively asking for feedback it helps to emphasize that you welcome criticism. That way an "Everything is ok." answer is less likely.

You can get more meaningful verbal feedback when you ask specific questions rather than really broad questions.
Consider broad questions like:

Am I good PhD student?
Is my dissertation good enough?
Does my research make sense?

vs questions like:

Do you feel I am on pace to complete enough work to graduate within X years?
Does the organization of my dissertation make sense?
Are the central premises of my research making sense to pursue further or should I be investigating?

So, start by asking better questions. Then you will have a lot more context surrounding the feedback you think you are getting. Likely this will resolve all your assumptions around the subject.

I cannot see the difference between the two sets of questions you propose. How are the broader questions better? They all seem to be very relative questions. In what way are you expecting the advisor to answer them?

@PedroTamaroff the claim is (in the first line) that specific questions are better, not that broader ones are better. I would agree and stress "questions with answers based on objective, specific measures" - to which the first one is a good example.

Maybe your supervisor sees a problem and wants you to figure out what the problem is? From your question, it is hard to evaluate whether this is the situation, but since no other question mentioned it, I will.
There is no greater success when mentoring to get the "mentee" to realize the problem and find his/her own solution. That is true learning. Just handing out the answer is extremely dissatisfying for any supervisor - unless said supervisor is acting more as a QA checkpoint than an actual supervisor.
Keep working on it. As an aside, if you feel he "ok's" too much, well bring him two alternatives and ask him which is better. That will help you as well, attacking the same problem from different angles often yield fruit.


text:
What computer specs should be included in numerical results section, when, and why?
Generally in the numerical section for papers I read, they include (if at all) computer specs like how much RAM, CPU speed, or type of processor.  What kinds of computers specs should I include? When should I include them? Why? Some general scenarios when you don’t need to include the specs would be helpful. As I am writing an interdisciplinary paper, I would also like to know about a possible dependency on the field.
For this question I would especially like to know why.  I know the basic idea is reproducible research, but are there really any cases where it is imperative to know it was an i5 Intel processor?

Certainly - I'm currently working on debugging something that's an OS-level issue and possibly tied to the processor. It would be helpful for someone working on reproducing your experiments to know that their AMD chip (for example) might be causing some differences.

Yes, it may matter that it was an i5; specifically, its cache size will affect lots of timings. For instance, matrix multiplication gets a lot slower when the matrices to operate on stop fitting in the cache.

@FedericoPoloni What if for my paper, I am only interested in comparing several methods and not necessarily the software-hardware interface.  I feel as long as all experiments are run on the same type of computer then it shouldn't make a difference.

Do you plan to report timings in your paper? If method A uses N memory entries, and method B uses 2N, then there is going to be a choice of the dimension N for which algorithm A will operate in-cache and B will no. So the timings will be skewed in favor of A, for a range of values of N that depends on the processor used.

@FedericoPoloni By same type of computer I meant exact same computer but I do get what you mean. The problem is since I don't how the guts of a computer then I don't know what others would care to know.  I will edit the question to maybe ask a better question.

Depends on what's the goal of reporting performance numbers and pc specs at all. If you don't know, likely that you want to show that your algorithm runs in reasonable time in the setting where it is meant to be used. E.g., if you're working on algorithms for image processing or CAD, you may want to show that the test system was a workstation similar to what the end user will have, and not a toaster or an IBM mainframe. Then, you can report CPU model number (e.g., Core i5-7500), amount of RAM, and maybe HDD model, if you read/write a lot of data.

@AWashburn: I took the liberty to remove your recently added question again, as this site is not suited for polls and collecting opinions. (Also, what is good practice should not really depend on the field here.)

A tangent issue: you should also care for statistical errors in measurement: warming up, mean vs. median vs. best time vs. worst time, discarding the outliers, such things. There are special tools to do most of this for you.

@OlegLobachev Is that why Matlab (and other softwares like R) will run a program several times and then take the average of the compute times?

@MathIsKey: Modern computers are complex, and the execution time for some piece of software can/will vary. One measurement alone will be useless. A computer may move from “saving power” to “running at maximum speed” to “overheating and slowing down”.

First, some (hopefully) obvious remarks:

you only need to present CPU specs if you are going to publish CPU timings
CPU timings depend on variety of factors, including hardware specs, OS, drivers, libraries, software, configuration of this software, etc. It is unlikely that the combination of all these factors will be reproduced exactly.
Even a very detailed list of hardware specs that can be published in an academic paper is likely to be incomplete: for example you can say that CPU is i5, but will you mention the rev? will you mention the microcode version? will you mention how well the CPU is cooled and how recently the thermopaste was changed?
The same is probably true for the software: it is unlikely that one can report versions of all drivers / libraries / software involved in the process.

It becomes clear that any publishable list of specs will not be sufficient to reproduce the settings. In my opinion, the exact reproduceability is not achievable and hence is not the aim.
I always provide and use the CPU specs as a rough guide that helps me to understand the behaviour of the CPU timings reported as a function of problem parameters. Can this slowdown be due to the data falling off the CPU cache? Can it be due to insufficient memory and hence disk swapping? Does this software allow to use all CPU cores or is only one core active? Can we (roughly) compare the timings reported in this paper with the timings reported by competitors, or are the setups too dissimilar?
At least once in my own experience I encountered an example of scientific fraud, when the reported timings could not be achieved on the declared CPU due to trivial complexity estimations — it could be easily shown that even at 100% efficiency the stated CPU could not perform the calculations needed in time claimed. In this case the details of the setup were handy to make the case.
Final note: reporting CPU specs is important, but making the code available is far more important and I wish people in academia did it more often!

Re: the final note: "code AND data".

I have realized that I know very little about computer specs but your final note has given me some clarity.  I will report CPU specs that most everyone reports and then make sure code (and data) are available in github or something like it

RAM could also be important, there is a world of difference between in-RAM computations and starting to swap out. Your penultimate paragraph is important in a broader sense, too: reproducibility is rarely of a primary concern, but knowing if the problem could even be solved at all in real-time or close using a described method could save a lot of time and money. If code runs at 10 FPS on RPi4, it may be fine for a robotics project, if these 10 FPS are achieved on 7950X instead, probably not so much.

@Lodinn This is right. In scientific computing we (almost) always  assume there is no swapping -- everything is either in GPU/CPU cache or RAM. When data hit swap it totally kills performance.

Yep, and then there is an issue of fitting data in RAM/VRAM in the first place, as not being able to do so often requires a non-trivial amount of tinkering. These days we have tensorflow OOMing, before it could be "if you have a similar data source to ours, you better also have at least 32 Gb of RAM to run this algorithm (and be happy it is not pushing 100)". For someone looking to modernize their lab, seeing these numbers in paper after paper is also extremely helpful to calibrate their expectations. Memory could really make all the difference between running an algorithm and not.


text:
What's the role of colloquiums in academia?
During my bachelors and masters degree I have attended some brief talks called colloquiums in which a researcher was invited by a professor at the university and s/he gave a one-hour talk about a scientific topic most of which about his/her own research interests.
These talks are a little different from normal conference presentations and seminars because the speaker is invited to give a talk and he is not presenting a specific paper; but in a conference, except from keynote speakers; people normally submit their papers to be peer-reviewed.
I don't know what's the role of colloquiums in academia and what the speaker is seeking by giving such talks? Also, who can give such talks (a researcher, somebody from industry, an outstanding professor, etc.)?

I would not consider a 1 hour seminar to be "such a small talks"

@ddiez I was under the impression that the OP meant "small" in terms of audience (which may or may not be true for colloquiums)

@xLeitix It was in my mind that in a colloquium, the speaker does not necessarily go into the depth of the topic he is talking about, and at least his talk is more brief [or smaller] than a conference/workshop presenter.

@xLeitix I see. I did not understood it that way because (as you mentioned) I have seen colloquiums with very small audience but also some that attracted a lot of people, even from neighboring institutes (depending on the speaker's reputation).

@EnthusiasticStudent ok, understood. However, at least in my field, most colloquiums are much longer talks that conference presentations (1-2 hours vs. 20-30 minutes)

Agree, colloquiums tend to be longer and more similar to keynotes than to conference presentations. At least that is my biased feeling.

In my field too. The duration of a colloquium is much longer than a conference presentation (about one hour or two) and the content is usually (but not always) not as deep as a conference presentation.

what the speaker is seeking by giving such small talks?

One word: exposition.
As a rule, the more senior a researcher gets, the less of her/his attention is devoted to writing single research papers. It becomes more central to be known for a specific niche, a specific topic where (s)he is the world's foremost expert. One does not become such a figurehead for a specific niche by writing good papers in the area alone (although, clearly, this is still required). One also needs to be an ambassador for the niche. This includes giving keynotes at conferences, as well as giving seminars and colloquia.

Also, who can give such talks

Everybody who gets invited by somebody. However, typically, one does not give keynote-level talks before senior postdoc or assistant professor level, simply because most PhD students do not have all too much of a vision going beyond their thesis yet. And, frankly, the keynotes of most postdocs also suck. Like most skills in academia, giving good exposition talks is also a skill that comes with training.
(there are exceptions, of course - I can think of at least one outstanding PhD student in software engineering who was regularly invited to give keynote talks at conferences midway through her dissertation)

A keynote talk at a conference is pretty different from a colloquium, though.

@NateEldredge Hmm. Not in my field, usually.

In addition to other answers, a colloquium series is often used as a way to indirectly fund research collaboration.
In many cases, the colloquium speaker is a collaborator of one of the institution's own faculty (call her X), or at least X is specifically interested in the speaker's work.  X may suggest that the speaker be invited.  The speaker will usually be on campus for a day or two (or more), during which he and X can have longer technical discussions.  The colloquium talk is for the benefit of the rest of the department: they can learn about the speaker's work at a higher, less technical level. (Colloquiua are usually meant to be pitched to an audience of faculty and grad students with a general background in the field, not necessarily the speaker's specific subfield; of course, that isn't always the way the talk turns out!)  The department pays for the speaker's travel expenses, and everybody is happy.  Next week, someone else's collaborator is invited.

Why do you call it a colloquium series. In most of the colloquiums I have attended, separate sessions had separate topics and the talks were not necessarily connected to each other.

@EnthusiasticStudent: It's a "series" in the sense that there is a talk each week at a regularly scheduled time.  But there is no relationship between the talks or speakers.

If a department has not regular talks and it has one talk per month; is it still called a colloquium series or just colloquium? Or these talks are not colloquiums?

In addition to @xLeitix answer, another reason comes from the inviting part point of view. For example, in my institute from now and then researchers (at the levels mentioned by @xLeitix) are invited to give talks. The goal is that researchers in the institute get to know about other researchers vision, topics and approaches. The aim is mainly to broaden our knowledge in order to stimulate new ideas and collaborations between researches from different topics or even fields. This is why typically these presentations are not just about a specific paper (likely niche topic), and look more like keynote presentations in conferences.


text:
Is it ethical/lawful to recolor/scale the logos?
Is it ethical/lawful to recolor/scale the logos when importing them to presentation slides to make the logos meet the template standards and fit the theme colors?

You should probably ask the submitter to fix them if it is very important.

What do you mean by "resize"? Resize with respect to what? Resizing the logo to occupy more pixels? More centimetres? Relative to the default text size? Are you even in control of the resulting size of the logo if you place the logo on slides that get projected somehwere? I mean ... depending on the distance of the projector to the projection surface, your slides can come out at any size.

@O.R.Mapper I changed it to scale.

@EnthusiasticStudent: It doesn't get any clearer like that. Rescale ... relatively to what happens to be the initial size after inserting the logo in, say, Word?

@O.R.Mapper I afraid I don't get what is unclear to you, but my question here is about recoloring from what the right holders have used in their websites, productions, etc to the color that match the presentation template; and to scale, for instance the size of the logo which is published in their official website is far bigger than a slide and the user has to scale it down to put it on his presentations. (To make it clear, I am not talking about a company, I am talking about my university's logo which I want to put on the title page of my presentation.)

@EnthusiasticStudent: What do you mean by "far bigger than a slide"? How "big" is a slide? Isn't a slide, when shown in full-screen mode, exactly as big as your current screen resolutions happens to be, which can differ from system to system? And, while that refers to the size in pixels, isn't the size in centimetres different again, depending on which monitor you use?

@O.R.Mapper My question is clear enough. It happens that logos are bigger than the normal slide size and the user needs to scale down the logo/figure/picture to make it fit the slide.

@EnthusiasticStudent: I repeat my question: How big is a slide? To me, it seems that if I display a slide at a resolution of 1024x768, the slide is 1024x768 pixels in size, if I display the same slide at a resolution of 800x600, then the slide is 800x600 pixels in size. With that in mind, the claim that the logo is "bigger than the slide" doesn't seem to make any sense.

@O.R.Mapper I can not get what is unclear here. We are not talking about projecting the slides which normally happens to scale up the hole slide. I am telling that the logo is bigger than our preferred size that we want to put in our slide page, so we need to scale it down to fit our desired size. Of course, with any resolution and size we discuss here; we can not waste half of a slide page with original logo size. One may want to put the logo in the footer line of every slide's page, so what? Does not he have to scale down the logo? The question is absolutely clear.

I see @O.R.Mapper has given up the effort to explain why "resize" is unclear, so I'll make one (and only one) attempt. When an entity, like the original logo, has no particular size (e.g., my university's logo appears on business cards, on billboards, and on lots of other things at various sizes), and when your use of that entity also has no particular size (projected on different screens, viewed on a video monitor, etc.), then "resize" doesn't mean anything.

Related: Putting altered social media logo icons on my website, can I get sued? on Webmasters.

I am confused on why this question got a lot of upvotes. Try as I might, I can't see how this is on-topic for Academia.SE.

Logos are often trademarked, and therefore you are not free to recolor them according to whatever color scheme your template happens to use.
However, many companies and universities do have multiple versions of their logo available, for precisely this reason. You should contact your university's (or organization's) press office (or similar office) to see what is available, before toying with it yourself.

If I am not mistaken, trademarking has nothing to do with this, as trademark laws are about who is allowed to use the logo (or something similar) at all. Legal problems may rather arise from the fact that somebody created that logo and a priori reusing it or creating a new work (recoloured logo) based upon it may be a copyright breach (depending on several factors such as country and what you use the logo for). Now, you are most probably authorised via your university or similar to reuse that logo, but there is no guarantee that you may also alter it.

Thank you for your answer. Does trademarking abandon the user to resize the logos too?

@EnthusiasticStudent resizing should not be a problem as long as the proportions are kept at constant. Note that there might be limitations on how small the logo can be resized, due to readability etc. Similarly you cannot crop a logo however you like, if you intend to put it at the edge of a slide for instance. As aeismail mentioned, a proper graphical profile should include variations in color, size and cropping.

Here's an example from The Ohio State University.

@posdef I think it is a good idea to post your comment as an answer because it was helpful to me indeed. Thanks.

@Wrzlprmft I believe trademark law does apply (at least, UK). Reason being that you are marking your trade (slides) with a logo deceptively similar to another company's. Of course, the intention is not to deceive, but the law exists to protect the company and I believe they're well within their right to object to the recolouring. But I'm not a lawyer. :)

@OllieFord: I did not say that trademark laws do not apply to the general situation of using a logo. But as you said, these laws’ whole purpose is to avoid that somebody uses the trademark who does not represent the trademark holder. So, if a trademark law prohibits you from using an altered version of the logo, it will also prohibit you from using the original logo. If you alter the logo such that nobody can confuse it with the original anymore (e.g., for satire), this may even be in your favour, at least concerning trademark laws.

@Wrzlprmft: I don't see any substantive disagreement between you and Ollie / aeismail, you just seem to be talking past each other. Yes, a typical logo will be protected by both copyright and trademark law, which both prevent you from using it, in any form, on a slide without permission from the copyright and trademark holder(s) (unless, of course, you can claim fair use or some similar right). Typically, such permission will come with conditions (often included e.g. by reference to a visual identity policy) that will forbid or at least severely restrict you from modifying the logo.

Most companies/institutions guard their branding very carefully.  Many companies spend thousands or even millions on developing a brand language, which includes fonts, colors, and other design elements.
I don't know the specifics of the legal ramifications of changing logo colors, but the owners of the logo are sure to be against it.

I am not sure whether there is any legal distinction (and input by someone who does is welcome), but at least logically, the statement "the owners of the logo are sure to be against it" only seems to make sense for logos that are coupled to a specific colour, as opposed to logos that just define a shape and are essentially "monochrome".

You bring up a good point, but occasionally monochrome is actually part of a company's branding.

@O.R.Mapper You call it 'monochrome', they call it 'specific colours: black, white' ;)

@OllieFord: I was not talking specifically about black and white, but about logos that are explicitly defined only as shapes, which are routinely integrated into different colour schemes. I am sure I have seen some examples in the past, though being generally bad with remembering brand names and similar, I cannot remember any of them now. Of course, some logos explicitly have only one (foreground) colour that still needs to be adhered to, no matter what.

@O.R.Mapper:  Thanks for the clarification.  I thought you meant that it is acceptable to color a logo in gray scale, which is not necessarily the case.  Typically, if it is acceptable to color a logo, the company will explicitly say so.

The following is based on general copyright concepts and should apply to any reasonable copyright laws:
Any logo is created by somebody, be it a professional graphic-design company or the dean’s nephew, and without further ado this person (or company) holds the copyright to that logo. This mostly means that you cannot do certain things with it (or an altered form of it), which usually include dissemination or using it for commercial purposes. Whether using the logo in a presentation shown to a small audience is included in this depends on your country’s copyright and other aspects. Using it in a publication would almost certainly be a breach of copyright, however. Anyway, let’s assume it would not be legal to use the logo for whatever you do.
As it would be pretty pointless, if, e.g., members of a university were not allowed to use its logo (when representing that university), the creator will usually have authorised the university and its members to use the logo – but this authorisation can be bound to conditions. Furthermore the university itself may impose conditions onto its members regarding the usage of the logo. These conditions may include:

You must not alter the logo (e.g., by recolouring it or changing its aspect ratio). I expect this to be a common condition.
The logo must make up a certain percentage of your slides, pages or posters. This is a rather silly condition in my opinion, but I would be surprised, if there were no precedent for this.
The logo must not be resized. If sufficiently stupid people make the rules, this might happen, however it hardly makes any sense: The logo may not have any physical size to begin with (as many image formats do not contain this information) and how the logo is initially sized when imported in your software depends only on whatever the software’s creator chose to be the default. And even if it has physical dimensions, it does not make any sense to use the same size in print and on projected slides. Something similar holds for sizes in pixels.
The word penguin must be on any page or slide on which the logo appears. I am exaggerating here, but the only way to be sure that there are no silly conditions is to check.

Now, if you are lucky, there exists some document which states that members of the university or similar are authorised to use the logo and which contains conditions (if any exist) and requirements of logo usage. Here is an example thanks to Mkennedy.
On the other side of the spectrum, you may have some institute’s logo, which was handrawn by the director’s niece 30 years ago and gone through several iterations of scanning and printing, because the original has been lost. You have no official authorisation to use the logo at all and the legal grey zone you are entering does not change much if you additionally alter the logo (in any remotely respectful manner). It is very likely that nobody will care, let alone sue you.
Where on this spectrum you are is something only you can decide.

Anyway, I would recommend to use such a logo only on one or two slides, so it should not dramatically destroy your colour concept.
Additionally, you might consider adapting your presentation’s colour scheme to the logo’s colour scheme, but beware that the latter is not necessarily a good choice for projectors.

I am converting my comment to aeismail's answer as the OP suggested:
Logotypes are typically covered by what's known as the graphical profile of organisations like companies, universities and indeed even political parties or NGOs.
A graphical profile usually contains things like (but not limited to) color(s), aspect ratio, font(s) and positioning of eventual text elements regarding a logotype in question. Depending on how "complete" or "strict" a graphical profile is, you can do varying degrees of manipulations.
It's typically not an issue to scale the image, given that the aspect ratio, or the width-height proportions are kept as the original. If the organisation in question has put some thought into their graphical profile, they should have the logotype in a vector-based format, which scales up/down without any quality loss.
Keep in mind that scaling up an image is usually not a good idea, if the image is bitmap and not vectorised. It's also good to remember that there might be issues regarding readability, i.e. there might be a limit on how much you can scale down the logotype. Logotypes that have text within the graphics tend to have such limitations. [Keen observer might notice how badly the text renders if one does not pay attention when converting vector graphics to raster graphics]
Finally, even if you are allowed to crop a logo (due use as decoration on the edge of a slide or poster) exactly how you can crop the logo might be defined as well. For instance the logo I linked above has 4 predefined cropped versions, that you are allowed to use. Beyond those you are not allowed to crop/scale/change the logotype in any way.
Just exactly how that might be enforced is a whole different story however.

Good answer!  Sometimes a different term is used to refer to a "graphical profile".  These may include the terms "style guide", "branding guide", or "design language."

To clarify the resizing issue discussed in the comments: for any length-based system of preparing a document, the concept of "size" is indeed a valid one.
If the people who provide official University logos have done their job properly, the logo will be available in postscript and/or PDF formats that have a defined size in centimetres. These original dimensions are probably intended for reproduction on A4 paper and in that case should not be changed (in the interest of consistency).
For instance, the .eps logo of my University is defined as being 1.77 cm high. On official, printed A4 letters it is the exact same 1.77 cm in height. In most cases it would be inappropriate to rescale this logo when creating an A4 document. Consistency is good.
As a side note, the concept of measuring Powerpoint slides in pixels is wrong: it's a vectorized document. There are no pixels. I don't have a copy of Microsoft Powerpoint, but Apple Keynote's default slide size is 1024x768 points (not pixels!). 1 pt = 1/72 inch. I suspect Microsoft's system is the same.
A logical method of scaling to different media would be to scale based on the font size of your main body of text. Most A4 documents have 10pt font. So, if you're producing a poster or presentation with a main font size of 24pt, just make the logo 2.4 times wider and taller. This will keep the logo's size in proportion to the rest of the text.


text:
How to answer a professor asking "what your masters work is about?" when my thesis is not in English?
I have contacted a professor, looking for a PhD position. He replied that my field is so near to his research field, and to consider my application; he needs to know more about my masters thesis.

I need to see your masters work.

My thesis and most of my publications are in a non-English language. I have one English paper published in a nation-wide conference's proceedings, and this only presents no more than a quarter of my master's thesis.
How should I reply to this professor to better describe my master's work?
PS: I have a detailed CV in which I have brought all my transcripts of my BSc and MSc, translated titles of my publications and projects and all my job experience.

UPDATE: I sent a copy of my English paper, my CV, my thesis abstract and some parts of my thesis besides to a research statement in which I wrote some research topics I am interested to work on.

From what you've said, it doesn't sound like he's asking for a translation. So give him a summary of your thesis, in English.

@mhwombat I think he wants to see my masters thesis... I don't know.

As a note: technical language is quite understandable, even for non speakers, as long as the languages are close enough.

The reason he might be asking to see your thesis is to get familiar with what you did, and to evaluate the quality of your work. If all the application materials (your CV etc) have English titles, he might not realize that the thesis is in any other language.
So, I would respond to him addressing what he needs, and not what he asked for literally. I would write an extended abstract (summary) of your thesis in English. I would send the following, the extended abstract, conference paper, and thesis. He can gain insight into all of your work from the thesis abstract you just wrote. Your conference paper evaluates your writing. And finally your thesis is sent just because maybe he understands the language. Even if he doesn't speak the language, the references, figures, tables, formulas etc. in the thesis should give him additional information about the content and quality of your work, especially in the context of your abstract.

I think that you should send the professor your thesis, pointing out the obvious that it is not in English.  Perhaps they actually know the language or have a trusted student or postdoc who does?
You should also offer to send an English summary and/or any papers that you have written related to the subject in English.

Don't second guess the request. You do not know his motivation.
Do you respond promptly?  Does he speak that language?  Does the document actually exist?
You obviously have command of the English language. Provide him a copy of the thesis in the original language,  give him a synopsis  in English.  He/she will follow up as appropriate..