What external funding opportunities are there for American or British students undertaking a PhD in Sweden? Consider an American and/or British (dual) citizen who is currently undertaking a PhD at a Swedish institution.

For instance, in America there are a variety of National Science Foundation fellowships available to graduate students in America.

Possibly, an American could use the Fullbright fellowship to fund their study at the Swedish university they are already at?

Higher education in Sweden is free at the PhD level, and free for EU citizens at any level. It is possible that an American who wants to take a Bachelor/Master level course as part of his PhD studies might need to pay a ~100$ application fee, but that should be it.

PhD here is a full time job, so your living expenses will be well covered by the salary. It also means that you would start the application by finding an open position through university websites, or approaching a potential supervisor to create a new position. Additional scholarships are aimed mostly to cover the tuition fees for students who do need to pay, or to finance brief research collaborations.


Anonymous Student Feedback: Moving forward from abusive comments I'm looking for some guidance from some senior/experienced academics situated in the teaching space.

As a relatively new lecturer (though I have some experience in lecturing in the past) I'm always looking for ways to improve my teaching. After doing a bit of research around how different lecturers etc approach student feedback, a number had stated that using anonymous feedback solicited during the semester can be really helpful.

So following on from that, I put on my learning site an anonymous feedback form for students that could let me know how they think the unit is going and what could be improved. The idea behind this was to get real-time feedback that could be implemented throughout the course, instead of for the following year.

I left this open since week 1, and perhaps that was my mistake. Despite barely being into week 2 (in which week 1 was just a basic introductory lecture to the course) one student has provided some anonymous feedback, which isn't feedback at all. Rather, it is just a string of abusive, sexist and foul language comments.

Due the alarming nature of the feedback, I approached a mentor/supervisor about what I should do. They were conflicted, because on the one hand, the nature of the comments were highly inappropriate, abusive, and raise concerns about the safety of my person in my teaching space. However, no specific threat at this time was made. On the other, having anonymous informal feedback throughout the unit is a good way of getting students to reflect on their own learning experiences without the fear of being reprimanded re assessments, and help me improve my teaching in the process.

I'm caught between continuing to allow anonymous informal feedback, or taking this option away and informing students of the reason. No other student has provided any feedback. Only myself and my teaching assistant have access to the feedback. Only students enrolled in the unit have access to the form.

Experienced lecturers/professors, how would you handle this situation?

This response here has been somewhat useful for future tips:

What is the best way to solicit teaching feedback from students (before the class is over)?

It sounds like you've encountered the well-known downside of online disinhibition effect, more colloquially known as GIFT.  There is simply nothing that can be done to prevent some people from being horrible when offered anonymity.

So, let's parse out two different questions about how you should react: 1) what actions should you take to protect yourself and other students? and 2) should you continue to use the anonymous feedback form?

Let's start with the first question: given that you know there is a horrible person in your class, what should you do to protect yourself and other students?
I see three basic possibilities here, either a) the person is insincere in their statements and is trolling you to try to get a reaction, or b) the person is genuine but not a threat, or c) the person is an actual threat to the safety of yourself or others.  If you are concerned that it might possibility c), then if there is any way for your IT staff to penetrate the anonymity and investigate, it's reasonable to ask them to do so, just as if somebody had made a serious electronic threat through some other channel (your institutions likely will have a process for reporting serious threats of harm, though, which may mean you shouldn't start by going directly to IT).  Otherwise, it's probably best to ignore it and move on, because otherwise you're just giving them the reaction and power over your actions that they are looking for.

Now, turning to the second: should you continue to use the anonymous feedback form?  The one problem with true anonymity is that if you get more nasty comments, you can't tell whether they came from the same person or not. I would recommend looking into whether you can get pseudonymous information from your system, i.e., so that Student X's comments are all collected together and Student Y's comments are all collected together separately.  That way, you can separate out and discard feedback from the horrible person (essentially marking them with a "troll" filter), while still getting meaningful feedback from other students who have more positive disinhibition from the anonymity.

It's a pity that some people are horrible and try to screw things up for everybody else; the rest of us need to try to figure out how to appropriately protect ourselves and our institutions while keeping them from profiting from this behavior.


Do having 2 masters increase the possibility of acceptance in a joint PhD program? Suppose, a research school in the USA offers a  Joint/Dual (Education + English) PhD  program. That university has an acceptance rate of 27%.

Understandably, someone with either an MA in Education or MA in English can apply for that program.

Do having 2 masters (MA in Ed + MA in English) increase the chance of getting acceptance in that joint PhD program for the student?

One of the answers here emphasizes that all else equal, the extra prep is better.  I understand this rationale but think you may actually getting the wrong idea from it.

Of course all else is not equal.  And at the end of the day Ph.D. programs are WAY more interested in talent than experience.  So emphasizing doing extra time in the salt mines to make up for past issues (and we get MANY questions down that line of thinking) is probably not an optimal strategy.

This is a key insight you need to consider.  Given the massive overproduction of advanced degrees (and the miserable job possibilities because of supply and demand), you really should not enter this "tournament" unless you have confidence that you are well above average.  If you need to be doing extra degrees to even get into the tournament?  Bad sign.

I realize this answer is both contrary and discouraging.  But I would at least consider the alternate insight.


How to provide letters of reference for a Postdoc Application I am applying for postdoc positions in the area of Mechanical Engineering (Robotics) in US and Europe. Often times I see that the reference letters are required to be submitted at the time of application and it is mentioned that only complete applications will be evaluated. Does this mean that I am expected to collect electronic versions of reference letters from professors, which I would need to submit along with my application? How can I waive my right to not review these letters?

What is the typical way of submitting reference letters for a postdoc application in the area of Mechanical Engineering (Robotics), since the application process is not so structured?

Frequently "reference letter are required to be submitted at the time of application" simply means that "get your letter writers to send their reference letters to e-mail address xxx@yyy.zzz before the submission deadline" (or upload to an appropriate website if the process is more organized).

This is in contrast to the situation in many faculty hires where the reference letters are solicited: in those situations you provide only the names and contact information of your letter writers and the hiring committee, after sifting through the files to narrow down the choices somewhat, then writes to the candidates' recommenders and ask for letters to be sent

That said, if you are applying for a PostDoc you are supposed to be young and naive and not know any better, so there's absolutely no harm in writing to the department/head of lab/hiring committee asking for clarification when you are not sure what the expectation is.


Who is earning money when users buy access to a research article? I'm a software engineer, but I used to have to read a lot of papers about neurology. I am slightly disappointed by the high price for access to the full text of research papers. I can understand a pharmaceutical company could pay that amount of money, but I don't think private people can afford to pay $30 for reading a paper.

Does the money from article sales go to the researchers, the journals or somewhere else?

Regardless of political considerations, the answer to your question is that publishing companies are collecting the revenue from subscriptions and individual article sales. Some publishers are commercial operations, some are non-profit (such as the American Institute of Physics).

The price you pay at this time is related only to the publishing process (organization of submission process and peer-review, typesetting, archival strategies, printing, administration, etc.) and all other aspects of a commercial company including, of course, a margin. Some people have strong opinions about what this margin should be.

The actual research is paid for by other means, including government grants, university salaries, non-profit and foundation grants, funding from commercial companies, etc. If you had to pay for the actual research behind a given paper, chances are it would cost you something closer to 30k $.

There are various other business models for publishing, some where the whole process is paid for by an institution, and is thus free for both authors and readers, some where authors have to pay to publish, etc.

There are important aspects in keeping money out of the editorial board - publisher - reviewers - authors relationship, because it reduces the risk of conflicts of interest (although author-pay open-access advocates think otherwise).

Options to obtain scientific papers without paying (yourself) include: going to the library of your local institution, checking out pre-print servers and government-sponsored repositories or ultimately, contacting the authors to obtain electronic copies. More on that subject in this useful thread.


Should I drop out of grad school? I have just completed my first year of grad school. I am currently in a enormous amount of debt from undergrad. I want to continue but I am afraid of accumulating too much debt! Yes I did know this going in about the increasing of debt. I have exhausted many options as far as looking around for financial resources. I am interested in the healthcare field.

No you should not drop out of grad school. The words "grad school dropout" doesn't fare well on any freshman CV. But, it must also be noted that after the initial slog, it also doesn't matter as much.

I have been told this statement many number of times by professionals who are at much better positions than me (a newly graduated PhD) in terms of both earnings and academic positions.

There is a real dearth of hard-working professionals in every profession.

I know it comes across as a bit philosophical, but I take it as a ground reality after having observed the same. Notice that I do not say talent, because talent seldom makes a person successful, hard work on the other hand almost always does. Also, a freshman with a degree always has more value than one with none.

Having said that, If you are worried about covering your debt after school, you should ponder these thoughts

Are you in a graduate course which makes for high employability?
If not, can you transfer to a course which offers high employability? CS and Informatics, are some examples of fields that offer very high employability.
Can you learn skills or enroll in courses which may increase your value in the job market?
Maybe try enrolling in one of the many free courses available online through portals like Udemy and Coursera to increase your value?

Invest as much time as possible towards learning as much as you can with employability being the focus. Furthermore, only learn that which you would love to do yourself because to get ahead in any field you need mostly determination and perseverance. You can only muster these two things if you love what you do. I do not make any mention of money here, because money generally follows people who get ahead in their respective fields.


How to obtain citation counts per year or exportable data from Google Scholar? To me, it seems that "Fully Convolutional Networks for Semantic Segmentation" (http://arxiv.org/abs/1411.4038v2) has quite a bit of impact. However, I would like to support this feeling with data.

On Google Scholar, I can see that the paper was cited by 116 publications. However, I would like to plot a curve showing when it was cited. So I would like to export this data to a CSV with a "date" column and a "title" column. (Other columns like authors, how often the other paper was cited, journal, ... would be nice, too).

Are there other ways to quickly analyze the impact of a paper?

Are you referring to this type of plot, which can be found through an author page containing that paper (in this case Evan Shelhamer), by clicking on the title:

At the Google scholar page of that paper you can also hover over the bars to see e.g. that the paper has 109 citations from 2015. I don't know about a way to directly export data from Google scholar. You could probably html scrape the data though, or by manually using "custom range" here to get citation counts for each year.

Another option is to use the Publish or Perish software (which functions as a front-end to Google scholar), which allows you to use the "Lookup citations" feature to get an exportable list of all the citing papers found through Google Scholar. This list will contain year, title, authors, citation count etc for all the citing papers.

An excerpt of the data for this particular paper is included below. In this particular case (since most of the citations are coming from arXiv) you can actually get a better temporal resolution of the citations after some post-processing of the exported data, since it includes the arXiv identifier which contains the month of publication (posting to Arxiv). That plot looks like this, for the 77 arXiv papers citing "Fully Convolutional Networks for Semantic Segmentation":

Data excerpt:

427,"K Simonyan, A Zisserman","Very deep convolutional networks for large-scale image recognition",2014,"arXiv preprint arXiv:1409.1556","arxiv.org","http://arxiv.org/abs/1409.1556","http://scholar.google.com/scholar?cites=15993525775437884507&as_sdt=2005&sciodt=0,5&hl=en&num=20",1,2015-11-18,""
51,"LC Chen, G Papandreou, I Kokkinos, K Murphy…","Semantic image segmentation with deep convolutional nets and fully connected crfs",2014,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1412.7062","http://scholar.google.com/scholar?cites=12556287530133233148&as_sdt=2005&sciodt=0,5&hl=en&num=20",2,2015-11-18,""
31,"B Hariharan, P Arbeláez, R Girshick, J Malik","Hypercolumns for object segmentation and fine-grained localization",2014,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1411.5752","http://scholar.google.com/scholar?cites=338188405356970854&as_sdt=2005&sciodt=0,5&hl=en&num=20",3,2015-11-18,""
24,"S Zheng, S Jayasumana, B Romera-Paredes…","Conditional random fields as recurrent neural networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1502.03240","http://scholar.google.com/scholar?cites=4680896688857314530&as_sdt=2005&sciodt=0,5&hl=en&num=20",12,2015-11-18,""
20,"J Dai, K He, J Sun","Convolutional feature masking for joint object and stuff segmentation",2014,"arXiv preprint arXiv:1412.1283","arxiv.org","http://arxiv.org/abs/1412.1283","http://scholar.google.com/scholar?cites=3867986733742388443&as_sdt=2005&sciodt=0,5&hl=en&num=20",4,2015-11-18,""
18,"G Papandreou, LC Chen, K Murphy…","Weakly-and semi-supervised learning of a DCNN for semantic image segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1502.02734","http://scholar.google.com/scholar?cites=12298732919189295864&as_sdt=2005&sciodt=0,5&hl=en&num=20",6,2015-11-18,""
14,"J Dai, K He, J Sun","Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation",2015,"arXiv preprint arXiv:1503.01640","arxiv.org","http://arxiv.org/abs/1503.01640","http://scholar.google.com/scholar?cites=10583411756105923851&as_sdt=2005&sciodt=0,5&hl=en&num=20",9,2015-11-18,""
13,"AG Schwing, R Urtasun","Fully connected deep structured networks",2015,"arXiv preprint arXiv:1503.02351","arxiv.org","http://arxiv.org/abs/1503.02351","http://scholar.google.com/scholar?cites=9137941562147447673&as_sdt=2005&sciodt=0,5&hl=en&num=20",10,2015-11-18,""
13,"G Lin, C Shen, I Reid","Efficient piecewise training of deep structured models for semantic segmentation",2015,"arXiv preprint arXiv:1504.01013","arxiv.org","http://arxiv.org/abs/1504.01013","http://scholar.google.com/scholar?cites=1420854562551446027&as_sdt=2005&sciodt=0,5&hl=en&num=20",25,2015-11-18,""
12,"S Ren, K He, R Girshick, J Sun","Faster r-cnn: Towards real-time object detection with region proposal networks",2015,"arXiv preprint arXiv:1506.01497","arxiv.org","http://arxiv.org/abs/1506.01497","http://scholar.google.com/scholar?cites=16436232259506318906&as_sdt=2005&sciodt=0,5&hl=en&num=20",5,2015-11-18,""
10,"G Bertasius, J Shi, L Torresani","DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection",2014,"arXiv preprint arXiv:1412.1123","arxiv.org","http://arxiv.org/abs/1412.1123","http://scholar.google.com/scholar?cites=2089551699301366907&as_sdt=2005&sciodt=0,5&hl=en&num=20",8,2015-11-18,""
9,"D Pathak, E Shelhamer, J Long, T Darrell","Fully convolutional multi-class multiple instance learning",2014,"arXiv preprint arXiv:1412.7144","arxiv.org","http://arxiv.org/abs/1412.7144","http://scholar.google.com/scholar?cites=6242051221514792488&as_sdt=2005&sciodt=0,5&hl=en&num=20",11,2015-11-18,""
7,"H Noh, S Hong, B Han","Learning Deconvolution Network for Semantic Segmentation",2015,"arXiv preprint arXiv:1505.04366","arxiv.org","http://arxiv.org/abs/1505.04366","http://scholar.google.com/scholar?cites=4896002303003783815&as_sdt=2005&sciodt=0,5&hl=en&num=20",80,2015-11-18,""
5,"M Jaderberg, K Simonyan, A Zisserman…","Spatial transformer networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.02025","http://scholar.google.com/scholar?cites=1662293494062093494&as_sdt=2005&sciodt=0,5&hl=en&num=20",7,2015-11-18,""
5,"R Girshick, J Donahue, T Darrell, J Malik","Region-based Convolutional Networks for Accurate Object Detection and Segmentation",0,"ieeexplore.ieee.org","","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7112511","http://scholar.google.com/scholar?cites=2674763949973029385&as_sdt=2005&sciodt=0,5&hl=en&num=20",42,2015-11-18,""
4,"S Ren, K He, R Girshick, X Zhang, J Sun","Object Detection Networks on Convolutional Feature Maps",2015,"arXiv preprint arXiv:1504.06066","arxiv.org","http://arxiv.org/abs/1504.06066","http://scholar.google.com/scholar?cites=8299550676813721451&as_sdt=2005&sciodt=0,5&hl=en&num=20",21,2015-11-18,""
4,"S Xie, Z Tu","Holistically-Nested Edge Detection",2015,"arXiv preprint arXiv:1504.06375","arxiv.org","http://arxiv.org/abs/1504.06375","http://scholar.google.com/scholar?cites=18154299256265143241&as_sdt=2005&sciodt=0,5&hl=en&num=20",112,2015-11-18,""
3,"W Liu, A Rabinovich, AC Berg","Parsenet: Looking wider to see better",2015,"arXiv preprint arXiv:1506.04579","arxiv.org","http://arxiv.org/abs/1506.04579","http://scholar.google.com/scholar?cites=11105541992267753132&as_sdt=2005&sciodt=0,5&hl=en&num=20",13,2015-11-18,""
3,"A Dosovitskiy, T Brox","Inverting convolutional networks with convolutional networks",2015,"arXiv preprint arXiv:1506.02753","arxiv.org","http://arxiv.org/abs/1506.02753","http://scholar.google.com/scholar?cites=3843085858101673825&as_sdt=2005&sciodt=0,5&hl=en&num=20",14,2015-11-18,""
2,"S Hong, H Noh, B Han","Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation",2015,"arXiv preprint arXiv:1506.04924","arxiv.org","http://arxiv.org/abs/1506.04924","http://scholar.google.com/scholar?cites=15385340253531275638&as_sdt=2005&sciodt=0,5&hl=en&num=20",15,2015-11-18,""
2,"O Russakovsky, AL Bearman, V Ferrari…","What's the point: Semantic segmentation with point supervision",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.02106","http://scholar.google.com/scholar?cites=14456480836534501375&as_sdt=2005&sciodt=0,5&hl=en&num=20",16,2015-11-18,""
2,"Z Xie, K Xu, W Shan, L Liu, Y Xiong…","Projective Feature Learning for 3D Shapes with Multi-View Depth Images",2015,"Computer Graphics …","Wiley Online Library","http://onlinelibrary.wiley.com/doi/10.1111/cgf.12740/full","http://scholar.google.com/scholar?cites=16653555319690091022&as_sdt=2005&sciodt=0,5&hl=en&num=20",17,2015-11-18,""
2,"P Wang, X Shen, Z Lin, S Cohen, B Price…","Joint Object and Part Segmentation using Deep Learned Potentials",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.00276","http://scholar.google.com/scholar?cites=4342156029683513177&as_sdt=2005&sciodt=0,5&hl=en&num=20",48,2015-11-18,""
2,"B Yang, J Yan, Z Lei, SZ Li","Convolutional Channel Features For Pedestrian, Face and Edge Detection",2015,"arXiv preprint arXiv:1504.07339","arxiv.org","http://arxiv.org/abs/1504.07339","http://scholar.google.com/scholar?cites=6994455475312011326&as_sdt=2005&sciodt=0,5&hl=en&num=20",59,2015-11-18,""
2,"S Gidaris, N Komodakis","Object detection via a multi-region & semantic segmentation-aware CNN model",2015,"arXiv preprint arXiv:1505.01749","arxiv.org","http://arxiv.org/abs/1505.01749","http://scholar.google.com/scholar?cites=17076919334968493616&as_sdt=2005&sciodt=0,5&hl=en&num=20",65,2015-11-18,""
2,"G Papandreou, I Kokkinos…","Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection",2015,"Proceedings of the IEEE …","cv-foundation.org","","http://scholar.google.com/scholar?cites=372687354279680428&as_sdt=2005&sciodt=0,5&hl=en&num=20",84,2015-11-18,"PDF"
2,"X Zhang, J Zou, K He, J Sun","Accelerating Very Deep Convolutional Networks for Classification and Detection",2015,"arXiv preprint arXiv:1505.06798","arxiv.org","http://arxiv.org/abs/1505.06798","http://scholar.google.com/scholar?cites=11183077033015235296&as_sdt=2005&sciodt=0,5&hl=en&num=20",89,2015-11-18,""
1,"A Khosla, AS Raju, A Torralba, A Oliva","Understanding and predicting image memorability at a large scale",2015,"","people.csail.mit.edu","","http://scholar.google.com/scholar?cites=4151583339195604249&as_sdt=2005&sciodt=0,5&hl=en&num=20",18,2015-11-18,"PDF"
1,"B Shuai, Z Zuo, W Gang","Quaddirectional 2D-Recurrent Neural Networks For Image Labeling",2015,"","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7118156","http://scholar.google.com/scholar?cites=13720065868238901658&as_sdt=2005&sciodt=0,5&hl=en&num=20",19,2015-11-18,""
1,"Z Liang, S Ding, L Lin","Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks",2015,"arXiv preprint arXiv:1507.03409","arxiv.org","http://arxiv.org/abs/1507.03409","http://scholar.google.com/scholar?cites=12791133750001877582&as_sdt=2005&sciodt=0,5&hl=en&num=20",20,2015-11-18,""
1,"D Pathak, P Krähenbühl, T Darrell","Constrained Convolutional Neural Networks for Weakly Supervised Segmentation",2015,"arXiv preprint arXiv:1506.03648","arxiv.org","http://arxiv.org/abs/1506.03648","http://scholar.google.com/scholar?cites=18113115400192563138&as_sdt=2005&sciodt=0,5&hl=en&num=20",22,2015-11-18,""
1,"C Ionescu, O Vantzos, C Sminchisescu","Matrix Backpropagation for Deep Networks with Structured Layers",2015,"","maths.lth.se","","http://scholar.google.com/scholar?cites=17387807402435828231&as_sdt=2005&sciodt=0,5&hl=en&num=20",23,2015-11-18,"PDF"
1,"V Badrinarayanan, A Kendall, R Cipolla","SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation",2015,"arXiv preprint arXiv:1511.00561","arxiv.org","http://arxiv.org/abs/1511.00561","http://scholar.google.com/scholar?cites=18037094217443794526&as_sdt=2005&sciodt=0,5&hl=en&num=20",24,2015-11-18,""
1,"K Lee, A Zlateski, A Vishwanathan…","Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1508.04843","http://scholar.google.com/scholar?cites=17230449095463437923&as_sdt=2005&sciodt=0,5&hl=en&num=20",26,2015-11-18,""
1,"B Pepik, R Benenson, T Ritschel, B Schiele","What Is Holding Back Convnets for Detection?",2015,"Pattern Recognition","Springer","http://link.springer.com/chapter/10.1007/978-3-319-24947-6_43","http://scholar.google.com/scholar?cites=1468500825478747183&as_sdt=2005&sciodt=0,5&hl=en&num=20",27,2015-11-18,""
1,"C Ionescu, O Vantzos, C Sminchisescu","Training Deep Networks with Structured Layers by Matrix Backpropagation",2015,"arXiv preprint arXiv:1509.07838","arxiv.org","http://arxiv.org/abs/1509.07838","http://scholar.google.com/scholar?cites=8704018611282114837&as_sdt=2005&sciodt=0,5&hl=en&num=20",28,2015-11-18,""
1,"Z Liu, X Li, P Luo, CC Loy, X Tang","Semantic Image Segmentation via Deep Parsing Network",2015,"arXiv preprint arXiv:1509.02634","arxiv.org","http://arxiv.org/abs/1509.02634","http://scholar.google.com/scholar?cites=18281955767933637624&as_sdt=2005&sciodt=0,5&hl=en&num=20",29,2015-11-18,""
1,"X Gibert, VM Patel, R Chellappa","Deep Multi-task Learning for Railway Track Inspection",2015,"arXiv preprint arXiv:1509.05267","arxiv.org","http://arxiv.org/abs/1509.05267","http://scholar.google.com/scholar?cites=16444267879523298138&as_sdt=2005&sciodt=0,5&hl=en&num=20",30,2015-11-18,""
1,"X Gibert, VM Patel, R Chellappa","Material classification and semantic segmentation of railway track images with deep convolutional neural networks,”",2015,"IEEE International Conference …","researchgate.net","","http://scholar.google.com/scholar?cites=13942078593779597868&as_sdt=2005&sciodt=0,5&hl=en&num=20",31,2015-11-18,"PDF"
1,"H Xu, S Venugopalan, V Ramanishka…","A Multi-scale Multiple Instance Video Description Network",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.05914","http://scholar.google.com/scholar?cites=1990066366497434516&as_sdt=2005&sciodt=0,5&hl=en&num=20",33,2015-11-18,""
1,"F Liu, C Shen, G Lin, I Reid","Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields",2015,"arXiv preprint arXiv:1502.07411","arxiv.org","http://arxiv.org/abs/1502.07411","http://scholar.google.com/scholar?cites=2562418167496300062&as_sdt=2005&sciodt=0,5&hl=en&num=20",52,2015-11-18,""
1,"PD Vo, A Ginsca, H Le Borgne, A Popescu","Effective Training of Convolutional Networks using Noisy Web Images",0,"comupedia.org","","","http://scholar.google.com/scholar?cites=12447971813084759439&as_sdt=2005&sciodt=0,5&hl=en&num=20",70,2015-11-18,"PDF"
1,"Z Zhang, AG Schwing, S Fidler, R Urtasun","Monocular Object Instance Segmentation and Depth Ordering with CNNs",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.03159","http://scholar.google.com/scholar?cites=7431213548054053779&as_sdt=2005&sciodt=0,5&hl=en&num=20",78,2015-11-18,""
1,"S Tsogkas, I Kokkinos, G Papandreou…","Semantic Part Segmentation with Deep Learning",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.02438","http://scholar.google.com/scholar?cites=16300824466121812385&as_sdt=2005&sciodt=0,5&hl=en&num=20",79,2015-11-18,""
1,"G Bertasius, J Shi, L Torresani","High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision",2015,"arXiv preprint arXiv:1504.06201","arxiv.org","http://arxiv.org/abs/1504.06201","http://scholar.google.com/scholar?cites=6429592123688911770&as_sdt=2005&sciodt=0,5&hl=en&num=20",86,2015-11-18,""
1,"M Havaei, A Davy, D Warde-Farley, A Biard…","Brain Tumor Segmentation with Deep Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1505.03540","http://scholar.google.com/scholar?cites=4159936825454045654&as_sdt=2005&sciodt=0,5&hl=en&num=20",91,2015-11-18,""
1,"P Fischer, A Dosovitskiy, E Ilg, P Häusser…","FlowNet: Learning Optical Flow with Convolutional Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1504.06852","http://scholar.google.com/scholar?cites=4399198863370102461&as_sdt=2005&sciodt=0,5&hl=en&num=20",111,2015-11-18,""
0,"CA Brust, S Sickert, M Simon, E Rodner, J Denzler","Efficient Convolutional Patch Networks for Scene Understanding",0,"hera.inf-cv.uni-jena.de","","","http://scholar.google.com/scholar?q=related:G0POBdhSJIsJ:scholar.google.com/&hl=en&num=20&as_sdt=0,5&sciodt=0,5",32,2015-11-18,"PDF"
0,"J Vašícek, M Hradiš, F Radenovic, O Chum","Camera Elevation Estimation from a Single Mountain Landscape Photograph",0,"cmp.felk.cvut.cz","","","",34,2015-11-18,"PDF"
0,"A Dubrovina, P Kisilev, B Ginsburg, S Hashoul…","Computational Mammography using Deep Neural Networks",0,"cs.technion.ac.il","","","",35,2015-11-18,"PDF"
0,"DL Richmond, D Kainmueller, MY Yang…","Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1507.07583","",36,2015-11-18,""
0,"X Liang, Y Wei, X Shen, J Yang, L Lin, S Yan","Proposal-free Network for Instance-level Object Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1509.02636","",37,2015-11-18,""
0,"M Xie, N Jean, M Burke, D Lobell, S Ermon","Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1510.00098","",38,2015-11-18,""
0,"S Gupta, J Hoffman, J Malik","Cross Modal Distillation for Supervision Transfer",2015,"arXiv preprint arXiv:1507.00448","arxiv.org","http://arxiv.org/abs/1507.00448","",39,2015-11-18,""
0,"H Chu, H Mei, M Bansal, MR Walter","Accurate Vision-based Vehicle Localization using Satellite Imagery",2015,"arXiv preprint arXiv:1510.09171","arxiv.org","http://arxiv.org/abs/1510.09171","",40,2015-11-18,""
0,"D Dai, Y Wang, Y Chen, L Van Gool","How Useful Is Image Super-resolution to Other Vision Tasks?",2015,"arXiv preprint arXiv:1509.07009","arxiv.org","http://arxiv.org/abs/1509.07009","",41,2015-11-18,""
0,"A Raj, D Maturana, S Scherer","Multi-Scale Convolutional Architecture for Semantic Segmentation",2015,"","ri.cmu.edu","","",43,2015-11-18,"PDF"
0,"F Xia, J Zhu, P Wang, A Yuille","Pose-Guided Human Parsing with Deep Learned Features",2015,"arXiv preprint arXiv:1508.03881","arxiv.org","http://arxiv.org/abs/1508.03881","",44,2015-11-18,""
0,"J Xie, M Kiefel, MT Sun, A Geiger","Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer",2015,"arXiv preprint arXiv:1511.03240","arxiv.org","http://arxiv.org/abs/1511.03240","",45,2015-11-18,""
0,"J Kim, V Pavlovic","Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks",2015,"arXiv preprint arXiv:1506.09174","arxiv.org","http://arxiv.org/abs/1506.09174","",46,2015-11-18,""
0,"C Sun, M Paluri, R Collobert, R Nevatia…","ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03776","",47,2015-11-18,""
0,"LC Chen, JT Barron, G Papandreou, K Murphy…","Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03328","",49,2015-11-18,""
0,"N van Noord, E Postma","Exploring the influence of scale on artist attribution",2015,"arXiv preprint arXiv:1506.05929","arxiv.org","http://arxiv.org/abs/1506.05929","",50,2015-11-18,""
0,"A Kendall, V Badrinarayanan, R Cipolla","Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding",2015,"arXiv preprint arXiv:1511.02680","arxiv.org","http://arxiv.org/abs/1511.02680","",51,2015-11-18,""
0,"C Wang, X Yan, M Smith, K Kochhar…","A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks",2015,"… in Medicine and …","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7318881","",53,2015-11-18,""
0,"PH Liu","Novel Convolutional Neural Networks for Deep Learning and Its Applications to General Image Classification",2015,"","pc01.lib.ntust.edu.tw","http://pc01.lib.ntust.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708115-214445","",54,2015-11-18,""
0,"S Bittel, V Kaiser, M Teichmann, M Thoma","Pixel-wise Segmentation of Street with Neural Networks",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.00513","",55,2015-11-18,""
0,"D Ravìa, M Bober, GM Farinella, M Guarnera…","Semantic Segmentation of Images Exploiting DCT Based Features and Random Forest",2015,"Pattern Recognition","Elsevier","","",56,2015-11-18,"HTML"
0,"H Nam, B Han","Learning Multi-Domain Convolutional Neural Networks for Visual Tracking",2015,"arXiv preprint arXiv:1510.07945","arxiv.org","http://arxiv.org/abs/1510.07945","",57,2015-11-18,""
0,"LC Chen, Y Yang, J Wang, W Xu, AL Yuille","Attention to Scale: Scale-aware Semantic Image Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1511.03339","",58,2015-11-18,""
0,"A Seff, L Lu, A Barbu, H Roth, HC Shin…","Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection",2015,"… Image Computing and …","Springer","http://link.springer.com/chapter/10.1007/978-3-319-24571-3_7","",60,2015-11-18,""
0,"AL Jones","Segmenting Microarrays with Deep Neural Networks",2015,"bioRxiv","biorxiv.org","http://biorxiv.org/content/early/2015/06/03/020404.abstract","",61,2015-11-18,""
0,"Y Wang, J Liu, Y Li, H Lu","Semi-and Weakly-Supervised Semantic Segmentation with Deep Convolutional Neural Networks",2015,"Proceedings of the 23rd Annual ACM …","dl.acm.org","http://dl.acm.org/citation.cfm?id=2806322","",62,2015-11-18,""
0,"BS Riggan, C Reale, NM Nasrabadi","Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition",2015,"Access, IEEE","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7270978","",63,2015-11-18,""
0,"A Carlier, A Salvador, F Cabezas, X Giro-i-Nieto…","Assessment of crowdsourcing and gamification loss in user-assisted object segmentation",2015,"Multimedia Tools and …","Springer","http://scholar.google.comhttps://link.springer.com/content/pdf/10.1007%2Fs11042-015-2897-6.pdf","",64,2015-11-18,""
0,"P Hu, D Ramanan","Bottom-up and top-down reasoning with convolutional latent-variable models",2015,"arXiv preprint arXiv:1507.05699","arxiv.org","http://arxiv.org/abs/1507.05699","",66,2015-11-18,""
0,"SS Mukherjee, N Robertson","Deep Head Pose: gaze-direction estimation in multimodal video",2013,"","ieeexplore.ieee.org","http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7279167","",67,2015-11-18,""
0,"MM Cheng, VA Prisacariu, S Zheng…","DenseCut: Densely Connected CRFs for Realtime GrabCut",2015,"Computer Graphics …","Wiley Online Library","http://onlinelibrary.wiley.com/doi/10.1111/cgf.12758/full","",68,2015-11-18,""
0,"J Pan","Visual Saliency Prediction using Deep learning Techniques",2015,"","imatge.upc.edu","","",69,2015-11-18,"PDF"
0,"A Dosovitskiy, P Fischer, J Springenberg…","Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks",2015,"IEEE Transactions on …","computer.org","http://www.computer.org/csdl/trans/tp/preprint/07312476-abs.html","",71,2015-11-18,""
0,"X Wu","An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation",2015,"arXiv preprint arXiv:1506.05849","arxiv.org","http://arxiv.org/abs/1506.05849","",72,2015-11-18,""
0,"LC Chen, G Papandreou, I Kokkinos, K Murphy…","SEMANTIC IMAGE SEGMENTATION WITH DEEP CON-VOLUTIONAL NETS AND FULLY CONNECTED CRFS",0,"stat.ucla.edu","","","",73,2015-11-18,"PDF"
0,"C Frogner, C Zhang, H Mobahi, M Araya-Polo…","Learning with a Wasserstein Loss",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1506.05439","",74,2015-11-18,""
0,"G Bertasius, J Shi, L Torresani","Semantic Segmentation with Boundary Neural Fields",2015,"arXiv preprint arXiv:1511.02674","arxiv.org","http://arxiv.org/abs/1511.02674","",75,2015-11-18,""
0,"Y Wei, X Liang, Y Chen, X Shen, MM Cheng…","STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation",2015,"arXiv preprint arXiv: …","arxiv.org","http://arxiv.org/abs/1509.03150","",76,2015-11-18,""
0,"H Lai, S Xiao, Z Cui, Y Pan, C Xu, S Yan","Deep Cascaded Regression for Face Alignment",2015,"arXiv preprint arXiv:1510.09083","arxiv.org","http://arxiv.org/abs/1510.09083","",77,2015-11-18,""


Is my authorship undeserved, and if so how do I address this in a professional setting? I am a current undergraduate who has been involved in a research lab for the past 3 years. Not too long after joining, I was asked to be a co-author on a paper that was already written. My PI wanted me to be recognized on a publication so I could add it to my CV, but I did not contribute or even read the paper until after it was published. I accepted this co-authorship at the time because I was excited to have a paper under my belt. Now, after gaining more experience working in academia, I realize that I clearly should not have been named a co-author.
My academic reputation is important to me as I hope to have a career in this field of research, so I want to make sure I am honest about my work. I will be applying to graduate schools soon and am concerned about how this co-authorship will affect my applications and my interviews. I have considered removing the publication from my CV, and I have also thought about keeping it on there while being honest about the situation if asked about it in interviews.
Do you have any advice on how to approach this situation? Thanks in advance!

What's past is past and can't be changed. It is good that you have learned about this issue, but I suggest that you ignore it and just list it like any other publication. There is no major harm done here, even though such "gift" authorship is frowned on in many (not all) fields.
If asked to explain it, just say that you were, at the time, less sophisticated as you were an undergraduate.
Alternatively, you could leave the paper off of your CV, but that can look strange if the paper surfaces.
But the bottom line here is that, as an undergraduate, inexperienced in such things, you did nothing wrong.
And, in some fields, a lot of people get listed as authors even if  their intellectual contribution was minimal, since their work is essential to carrying on the work of the lab. Whether that applies in your case, I can't say. Your advisor, PI, might be worth having a conversation about this issue. They may assure you, or not, but their opinion is worth hearing.

I need to add a few things for context. There are certainly a lot of people who don't accept what they call "gift" authorship in any form and so hate on answers like this one. But the question of what is a gift and what is earned can be subtle. In most fields, where one or a few people work together, without need of extensive and long running laboratories with all of the technicians that are required for that, it is a pretty simple question and gift authorship is heavily frowned on in those fields.
But there are some fields in which nothing gets done without a lot of people contributing over a long period of time. When a paper comes out of CERN, for example, the list of authors might be very long. An experiment might take years to set up and require hundreds of technicians, without whom there would be no breakthrough at all. The work is simply impossible without them. In those situations, lots of people get authorship for their contributions.
There have been a few papers, in fact, where the list of authors has been longer than the paper itself. But, in those fields, it is understood what it means and the order of authorship might be vitally important to help sort it out.
It is, I think, a mistake, for those in other fields with a different work process, to judge these people.
See, for example this paper with more than 5000 authors


Should I require a student to acknowledge or change a color scheme from a common paper? When I wrote my first paper, I experimented a bit with different colour styles until I found one that was both clear and aesthetically pleasing. I’ve since used it for all papers where I was first author. One of these submissions has been with a master’s student, who is now writing up his thesis.

He used the colour definitions from the paper for the figures in his thesis, which slightly irks me. Partly it is because this makes it harder to see which figures are cited from the paper and which are his own, partly because I put some effort into it. Is this so minor I should just let it go?

Update: As Wrzlprmft pointed out, using a consistent color scheme within a thesis is important for readability. Since recommending to use something and then requiring acknowledgement would be weird, I let it go.

Unless I totally misunderstand what kind of colours we are talking about, your student did what I would consider best practice. Using a consistent colour scheme for all figures in a publication or presentation is a good thing for the following reasons:

It is aesthetically pleasing, in particular on posters and presentations.
If two plots have colour axis for the same variable or measure, it facilitates comparing them.
If colours encode the same things across plots (e.g., red is always species A, blue is always species B), it increases readability, as the reader does not always have to check the encoding for each figure.

This also holds across publications, if they are about the same topic.

  this makes it harder to see which figures are cited from the paper and which are his own

Citations should suffice to indicate this. You cannot rely on stylistic mismatches to detect images from other sources anyway.

  I put some effort into it

As Jakebeal already noted, developing a colour scheme is rarely a scientific achievement. And even if you consider it to be one, your student can cite it and reuse it. After all, being reused is what scientific results are for. Remember that many scientists use plotting and typesetting programs developed by other scientists.

An acknowledgement does not hurt though, but I would probably not be very insistent on it.


Do STEM professors retire from research at age 65, while continuing to teach classes? I see many STEM professors work well into their 70s, 80s, and sometimes even into their early 90s - mostly in teaching, but their research careers were outstanding.  One of them that I spoke with told me that he hasn't done research in many years - though he teaches a class every semester.

Do STEM professors "retire" from research at age 65, while continuing to teach and enjoy their tenure?  Or are many professors still active in research past retirement age?

I'm asking primarily about US universities.

This is very much a function of the regulations of the institution in question, and the health and desires of the faculty members in question.

I have known a number of faculty members who start to wind down their research groups at age 65 or even a little earlier, while others wait until about age 70. I also know of schools that don't allow faculty members over 65 or 70 to bring on new students, thereby effectively "shutting down" the groups. But I do know a handful of faculty members who were active as researchers into their 70's and even into their 80's.

In countries other than the US, policies are much stricter. In Germany, for instance, professors must retire at 65 unless their successor allows them to continue to supervise students and conduct research.


Giving up before even starting: life without a PhD I am currently a Master student working on my thesis (started a few days ago). I've always wanted to become a researcher (thus getting a PhD) but a few months ago I started doubting about this decision and about my potential. Now that I'm working on my thesis, this feeling is getting just stronger and stronger. I thus came to the conclusion that I'm most probably just a mediocre researcher that would most probably be able to get accepted into a barely mediocre graduate school (I'm in Europe), even if my grades are quite strong. At this point there isn't much I can do, like getting a degree that would lead me more easily to getting a job in Industry, so I'm quite worried about my future and I lost all my motivation.

So, my question is: how likely it is, for a mediocre (Master) student and if we don't consider getting a PhD, to be able to get a job in Industry (like R&D department) in fields like data science or bioinformatics? If you will: what's the point of producing mediocre (research) results?

I read a lot of questions on this community about people in the middle of their PhD having similar doubts, but in this case I haven't even started it, so probably it's not a duplicate question.

It's quite likely you'll be able to get a job in industry with a mediocre Masters.

Think about it this way: there's a sizable population that never finish their Bachelor's degree. Among those that did complete their Bachelor's, a sizable fraction never do a Masters. In other words, you are already quite rare. So you'll probably be able to get a job in industry, if only because you don't have many competitors. Another way to think about it is, given that you're doing Masters, I'll guess that you did quite well during your Bachelor's. Odds are you know people who did worse than you (i.e. had "mediocre grades"). Did they manage to get a job? You can check, although I predict the answer is "yes". They might not have gotten their dream job or one that pays as much as they want, but they got something.

It sounds like you are asking this question because you're unfamiliar with the job market. Therefore I suggest searching for jobs in your vicinity that require a Masters degree in whatever field you're studying. It's imperative to know, because then you'll have some idea of what you're going to be working as after you graduate. If you find there're lots of vacancies, your chances of getting a job goes up.

One more thing: don't view not getting a PhD as "giving up". I know quite a few PhD holders who, in retrospect, think they should not have gotten a PhD.

  What's the point of producing mediocre (research) results?

I don't know, do you? If you don't know either, why are you even considering it? You're in charge of your own happiness; you have to alter your own fate.


Research interests Vs Statement of Purpose(SOP) I am not sure if this question is too specific. Please feel free to mark it as such if it is.

I have come across a PhD advertisement that says "upload a curriculum vitae including a brief description of your research interests".

Does 'brief description of your research interests' means SOP? I can see that research interests and research experiences are different. Is 'brief description of your research interests' different from SOP?

The intent of both types of response is similar enough to be identical, but there's perhaps a more important distinction here: this is part of a job application. The audience for this application is interested in what you have already researched, why you studied it, what you plan to research next (ideally as your PhD research), and how your past experience will aid your research plans. It's an opportunity to connect some of the material already listed on your CV, especially as CV's generally don't include much detail about the future.

This previous question ("Unclear terms: Statement of Purpose, Academic Statement and Personal Statement") touches on similar issues though it appears that the poster never received an answer they liked.


How should a job market candidate leverage his or her network? I am a job market candidate who has really “worked” conferences for the past 4 years. Now when I go to conferences, I find it easy to have coffee with a variety of junior (and some senior) professors in my field from other universities. I get invited to their dinners. I chat with them easily at the cocktail hours. Now that I am on the job market, I am not sure how to use those contacts to their fullest potential.

I mentioned this to my chair (whose work is in a different subfield than mine is), and he said I should not contact my network about potential jobs at any particular university because I would sound too “desperate”. This strikes me as incorrect, or at least incomplete, advice.

Should I let proactively let people in my network know that I am on the market? If so, what is a good strategy for emailing people in my network and letting them know I am on the job market? (Do I send around my job market paper and CV? A link to my website?) Is there anyone I should not email?

I should not contact my network about potential jobs at any particular university because I would sound too “desperate”.

Oh, please. Your chair probably remembers the Good Old Days when you could submit three applications with your one publication "in progress" and get two interviews and four offers.

Yes, of course you should contact people in your professional network.  The academic job search is no time for shyness or modesty. You need to sell yourself. You need other people to sell you.

Ask them if their departments are planning to hire, and if so, what their hiring priorities are. (And no, you cannot get this information from the text of their ad.)
Ask some of them to write recommendation letters on your behalf.  At least in computer science, you must have at least one reference letter, preferrably more, from someone who is not from your home institution and not a coauthor, but who can still write in detail about the quality, depth, originality, and impact of your work. Congratulations, you've cultivated the perfect environment to find such people.
Write them to let them know you've applied, and ask them (directly, shamelessly) to put in a good word with their hiring committee.  You will not get an offer from any department, and you may not even get an interview, without a local champion inside that department; the champion doesn't have to know you personally, but it certainly helps.  (Of course, a better way to cultivate a champion is to knock your interview out of the park.)
Always thank people for any information or help, even if they just say "Sorry, we're not hiring in your field this year." or "Good luck!"


Placement of numerical references (“[1]”) I kind of struggle with my students. I always told them to put the reference number (identification) close to the cited object - to make really clear what is the cited object. For example:

The theorem:

  Theorem A [1] is great ...

The study:

  In the study [1] it is possible to see that ...

The statement:

  It was proven that usage of ... leads to problems [1].

However, my students keep doing something like this:

Citation is related to the previous sentence! (probably):

  ... and it was proven. [1] The general idea ...

As above, but with extra period:

  ... and it was proven. [1]. The general idea ...

After the entire paragraph:

  ... and it was proven. [1]

They are arguing that this referencing is correct according to the ISO norm/suggestions. They keep doing that even though I told them to not do so. And they generally try to comply with my guidelines. This is a real exception. It seems this referencing idea is deeply rooted in their brains.

My questions:

Is their way of referencing legal? If yes, where are informations how to read this fuzzy notation? And why it does not appear in top journals?
How to tell them that this referencing is really unclear in a way that they will understand.

Note: The students I am struggling with are from all continents/countries, pursuing a university degree in electrical/software/mechanical engineering. I would say they are mostly 22±2 years old.

The universities with which I am familiar decide at the faculty/school/department level which referencing style will be used in courses that they teach/manage. This means that:

a standard referencing format is set across all courses and classes within that area
students do not have to struggle with different formats within the same subject
referencing guides with comprehensive examples are readily available for the stipulated referencing format
there is no need to argue/justify/negotiate with variations

If this is not the case, choose a popular referencing format (Vancouver style uses numbered citations) and apply that. Give the students a link to a Vancouver style guide such as this, as well as handing out a printed copy at the beginning of the semester.

When/if they then argue that their referencing is correct it can be compared to the established style guide.

To be truly effective (in changing habits) the referencing needs to be included in the assessment rubric.


Which approach to studying is better? This is not a usual question on this site, but I think it is appropriate nevertheless since I believe that members of this forum have vast experience on studying and all things that affect studying.

I am a student and I have a "problem" that's basically this: when I start studying something I feel unpleasant and restless if I am not completely sure that I understand the content being studied. This sometimes leads me to spend "too much time" on a particular subject and one might argue that it is a waste of time. My personal opinion is that this is the only way to study, I don't go to the next topic unless I have completely understood everything that's presented and unless I've come up at least with one question that I have to resolve in order to proceed. Sometimes the number of questions increases and usually that's what prolongs the studying of that particular subject.

However, many of my fellow students seem to be satisfied with just a basic understanding of the topic, and the questions that are keeping me from continuing, often don't bother them at all.

What is the better approach to studying in your opinion and why would that be? Am I being too perfectionistic or am I just trying to have a complete understanding of the topic?

It is good if you can understand something completely and thoroughly. However, there comes a point where most of us must leave gaps and accept that the gaps will fill with extended experience and exposure, over time (technical term: "by osmosis"). Failure to accept that gaps need sometimes to be tolerated can lead to collapse of careers.

What you need to judge is how fast you are moving - if you do not trail back too much with respect to what's required in your duties, I would not worry too much. Good understanding is a worthy cause and will pay dividends later on. However, when you fall back or fail to address important aspects of your studies (or repeatedly fail, through perfectionism, to submit reports/works/papers on time), then you should consider ways to get a coarser, higher-level view of what you need to learn and start to leave gaps.

Hopefully these criteria will help you to judge at which level you may want to operate and to switch between levels when required.


Why do US flagship state universities vary so much in quality? In the United States, it is typical for each state to have one or more flagship state universities. All of them are reasonably good institutions, but some are absolutely top-notch, such as in Michigan, California, Virginia, and Wisconsin.

Typically, tuition at these universities is extremely expensive for out-of-state students and much cheaper for in-state students. So, at least in principle, these universities principally serve the best students from their respective states who choose to study at public schools.

How is it then that some schools became absolutely top-notch and others less so? To my mind there is no obvious factor explaining which of the state schools became top-notch; for example, Wisconsin is not, and never has been, an unusually populous or wealthy state.

Different states have very different political attitudes towards their flagship schools as well. This makes a difference with funding levels of course. It also makes a difference in terms of how money is to be used. A governor/executive, combined with a university president, that wants to grow a flagship institution can help earmark funds to attract top-notch researchers, link business activity and academic areas to grow in, and so on. It takes planning and vision as well as money, and that varies a lot from state to state.


Right way of requesting research project from a professor as a non-affiliate I am a new immigrant and my question is about norms in American universities. I have MSc degree from abroad and in order to improve my chances for admission to a decent PhD program I would like to get published. I would appreciate your answers about the following questions:

Is it acceptable for someone outside a department/university to contact a professor and ask him/her for a research project (as a non-student)?
I do not have journal publications. What else can make them convinced?
Finally, what would be an ideal etiquette/strategy for contacting professors with a plan for making such a request, if it is sensible at all.

It is acceptable, but not the best strategy. Getting the attention of a top professor is like fishing a very picky fish: you need a good bait. In my experience, it is easier to approach them by offering something rather than by asking for something. Also, the approach should be genuine. This is, you approach them because you want to work on something that might fit his/her research interests not just because you want to see his/her name tied to yours in a paper.
IDEAS: Professors salivate when they see an interesting idea.
Just follow all the etiquette rules. Be polite and respectful as you would be with any other person. Also, try to be precise, they are busy people.

More importantly: Publications are not strictly necessary to be admitted to a decent PhD.

If you have some project in mind, write it and send it to the Professor in question. Tell him/her that you are interested in working on such project and that given his/her research interests, you think that the project would be greatly benefited with his/her ideas and that you would appreciate collaborating with him/her on it.


Pitfalls of Academic Blogging I am a junior academic (just finishing a PhD in computer science) and I'm considering starting an academic blog. My reasons for starting a blog are:

Fun.
Improving my visibility.
The benefit of science.

… basically, the same reasons I publish papers. (In a sense, academic blogging, is just a 21st century form of academic communication to supplement journals and conferences.)

However, I also have concerns:

Maintaining an active blog takes a lot of time and effort. (i.e. it may stop being fun.)
Fear of failure - many blogs die. (Does an inactive blog look bad?)
Being a junior researcher, I worry that people will not care about my blog or, worse, disagree with what I say.

So I want to ask for advice from people who have (or had) an academic blog:

Is academic blogging a good idea?
Does it become too much effort?
Is it worthwhile?
How likely is blog-death?
In general, what are pitfalls to watch out for when starting an academic blog?

First of all, I'm not an academic yet, but I'm planning to be. I read a lot of academic blogs in my field daily or should I say, semi-academic. By semi-academic I mean that they have articles of an academic subject but with more in-layman's-terms approach. And I absolutely love all of them. I believe that being able to explain things simply is an art, not mention extremely valuable, and having a similar blog is something that I aim to do in the future.

To address your concerns:

  Maintaining an active blog takes a lot of time and effort. (i.e. it may stop being fun.)

Yes it is. I've been blogging about tech (Android and Linux) in the past and after a while it became an extremely cumbersome experience maintaining a steady flow of news, reviews and opinion articles. BUT this is not what an academic blog is all about. You don't need a good article every day. You need a good quality article once or twice a month. This is not so hard.

  Fear of failure - many blogs die.

They do. And yours might too. So what? Firstly, you shouldn't fear leaving your blog inactive for a couple of months. This is something you want to do, remember? If you don't have the time or you just don't feel like it for a while just don't do it. If you don't like the view of an abandoned blog after a few months just delete it. It's ok.

  Being a junior researcher, I worry that people will not care about my blog...

Producing quality content is the key here. If you produce quality articles from which your readers learn stuff you'll find out that after a while a significant amount of them will care.

  ...or, worse, disagree with what I say.

That's the best part! Being a junior academic you already know that:

Everything you say must be backed with evidence
No matter the evidence some people will disagree with you

Disagreeing means views and comments. Disagreeing is how you'll make regulars. This is a key concept in blogging. Rarely you will get comments like "I completely agree with your article", not to mention that they don't have a special value. But comments like "I think that you forgot to include the fact that X" sparkle interesting conversations from which both parties learn.

To answer your questions:

  Is academic blogging a good idea? Is it worthwhile?

Yes, it's a great idea and it will be worthwhile for you and your readers especially if your articles sparkle interesting conversations. An academic blog indeed increases visibility and creates a more friendly/casual view of the writer which in my opinion most of the times is very much needed.

  Does it become too much effort?

Not if you limit yourself to one or two articles per month. Your goal is quality, not quantity. You're not a news site, you're an academic.

  How likely is blog-death?

Very. So what? Just do it.

  What are pitfalls to watch out for when starting an academic blog?

Do it because you want to do it.
Don't burn yourself out.
Don't get stressed over the quantity. Aim for quality.
Use a simple language.
Be fun.
Have fun.


How do multiple PhD scholarships combine? I'm currently in the process of applying for various scholarships to fund my PhD.

Since I'm writing to many different organizations/institutions/etc., I started wondering whether these different funding schemes can be combined. I found it hard to find any reliable information on this matter, so I reckoned I'd ask it here:

Suppose I successfully obtain two scholarships, in two different countries, amounting to roughly $15.000 and $25.000 annually, both excluding coverage for university tuition fees. Should I notify both of them of having obtained the other? Or should I make absolutely sure they'll never find out :) ?

I suppose there'll be severe consequences to keeping it silent -- if so, what?

Dave's answer is mostly correct: you should not accept both without the explicit written permission of both scholarship providers.

Some scholarships can be combined, if the fellowship sponsor allows it. However, in general, large-scale fellowships cannot usually be accepted in conjunction with other fellowships. You'll need to choose one or the other.

On the other hand, if you're talking about actual scholarships—by which I mean cash awards directly to you, without being tied to a specific course of study at a specific university—the rules may be more relaxed.

The important thing is to read the rules and conditions associated with each award, and then to ask the sponsors if you have any questions.


How were people able to graduate with a PhD at a such young age in the past? When I read up the bibliography of the mathematicians and scientists in the early 19th to 20th century, I am always shocked at how they were able to graduate university with PhD(s) at such a young age.

For example, John Von Neumann graduated from two universities simultaneously at the age of 22 with two PhD degrees and critical papers published.

John Forbes Nash graduated with a PhD at 22 on game theory. Enrico Fermi finished school at 21 with thesis on X-ray diffraction. Kurt Gödel finished school at 23 with dissertation on predicate calculus. Abel finished at 20 with thesis on quintic equations. Galois never graduated but created a field of his own before dying at the young age of 20 and the list goes on and on...

I am not knowledgeable of the trend in the social sciences but I suspects much of the same.

Can someone tell me if there has been a major shift in how university
education is conducted such that university across the world takes
longer to get through than it used to be
or is it truly because
these people are unusually talented and are able to get through
university purely based on their almost unnatural abilities?

Most importantly (and relevant), why is there such a decline in these young PhDs (I've never heard of a professor at my university who graduated at 21) and is it still possible for people to graduate at such a young age nowadays?

There are several issues:

Your examples are somewhat biased, since you've selected researchers who were not only brilliant but also prodigies.  By contrast, Riemann finished his Ph.D. dissertation at the somewhat older age of 25, and Weierstrass didn't start his research career until he was 39 or so.  Brilliant researchers are more likely to get an early start than average researchers are, but the correlation is far from perfect.  Focusing on prodigies gives a misleading picture.
There are plenty of prodigies today (Terry Tao, Noam Elkies, Akshay Venkatesh, Erik Demaine, etc.).  If they occupy a smaller fraction of academia, it's probably because academia has grown enormously even relative to the population.  The faculty in the top departments today are stronger on average than a hundred years ago, but there are many more departments nowadays, so the average professor is overall probably not as talented.
The amount that's known is steadily increasing, so getting to the 19th century research frontier takes quite a bit less time than getting to the 21st century research frontier.  This is not as relevant in very new fields, but finishing a Ph.D. in algebraic geometry in two years is a more impressive feat today than it was a hundred years ago.  This increase in knowledge doesn't translate perfectly into an increase in time to degree: people also compensate by specializing more and then broadening later in their career.  However, the obstacles to a quick Ph.D. are certainly increasing over time.


Why does a PhD take a longer time in some countries? I am talking about computer science. Recently I did survey on my own i mean informally. I find out that there are some researchers who were able to do PhD in same area in 4 year as compare to other researcher who did their PhD in 6 or 6.5 year's in the same research area. Is it depends  upon the duration of the PhD of supervisor or it depends upon the country etc.

Question : Why does a PhD take a longer time in some countries? What factors affect this? Does it affect the research quality? I mean if I am taking more time I should have more quality research.

Many factors that vary systematically between countries may affect time to graduation.

Here is an incomplete list:

In the U.S., grad school starts with studying for an MA degree; in Europe, an MA is a prerequisite to joining grad school
whether or not PhD studies include classes
availability, generosity and length of funding, vs. necessity to earn a living while working on the PhD
incentives to delay graduation vs. incentives and opportunities to join the labor market as soon as possible
quality of supervision and extent of leeway to pursue own interests and potential dead ends.

I'm not aware of any empirical studies testing each factor's relevance.


I successfully completed degree called Aspirantura from Russia. is it equal to PhD in Europe and US? I completed a four-year Aspirant course and successfully defended my Aspirant thesis

Apparently you don't have a Kandidat Nauk degree (otherwise you should have wrote about it), so by no means it is equivalent to PhD. Aspirantura without a Kandidat Nauk is not valued even in Russia.

Traditionally, Kandidat Nauk dissertation and degree was almost the only outcome of study in aspirantura. However, completion of aspirantura by itself did not automatically result in dissertation defence and awarding a degree. There were and there are a lot of people who do not submit and defend a dissertation while in aspirantura, so they would complete aspirantura without getting a degree. In this case they usually would not even get any diploma. (Some of them would defend a dissertation later, some would outright drop.) There were no final exams in aspirantura, and often no advanced curriculum; and in this sense, aspirantura was more kind of a bureaucratic device that allowed to get funding, give 'legal status' to aspirantura students, not an education institute itself. The main aim of aspirantura was not to give education to students, but to allow them to do research.
However, in recent years, Russian government (Ministry of Education, I suppose) decided to fit aspirantura into the main framework of Russian Federal Law on Education. In particular, AFAIK, aspirantura students are now awarded some diploma on successful completion of aspirantura. It is not tied to Kandidat Nauk dissertation, and has much less requirements, and mainly signifies that some person has indeed studied in aspirantura, and completed some aspirantura courses, and maybe some simple final exam. Even some thesis may be required, but I guess that requirements for such thesis are much lower than for a candidate dissertation.
This aspirantura diploma is not really valued in Russia. (On contrary, it can be a somewhat negative signal, because successfull aspirantura students will defend a Kandidat dissertation and thus will have Kandidat Nauk status, so if somebody has only aspirantura diploma and not a Kandidat Nauk one, this means that they tried and failed). The only use case of aspirantura diploma that I have heard of is when one has to prove that he had received a specific number of years of education, usually when they apply to some position in another country. For example, I've heard it used when one applies to European medical PhD programs. They require that applicants have, e.g., 8 years of medical education (the figures are approximate), while Russian medical universities only provide e.g. 6 years, so additional 3-4 aspirantura years make for the difference.
So if you only have an aspirantura diploma (and defended some thesis required for this diploma), then it is not a Kandidat Nauk degree, and thus absolutely not a PhD equivalent.
Disclaimer: I do not have any direct relation to Russian science in recent years, so my knowledge is based on rumors and information from my friends, many of whom are still working in scientific institutes, and some study in aspirantura, and so the information above may be not exactly correct.


Is it advisable to self-advertise in conferences being on the job market While giving presentations in conferences or talks in research meetings, is it ok to self-advertise that I'm on the job market (academic or industry)? Is it advisable or does it make more sense to mention that during networking in teams?

It is ok, but it may not be ideal.  Academia often frowns upon open self-promotion (more than it probably should, in my opinion), and flat out saying that you're on the market in a public presentation seems a little...well, "desperate" is too strong, but the next thing, perhaps.

Some thoughts about how/when/whether to do this:

Cultural nuances can vary in very subtle and localized ways.  If you are going to a big conference in your profession or field, there must be other people there who are on the job market.  Look to them and see how they are behaving: is anyone else advertising their availability in their presentations?  If not, maybe don't do it yourself without an especially good reason.  If so, look to the audience members and see how they're reacting.  A little face-reading will probably tell you whether people are happy to hear this kind of information.
Consider trying to disburse this information slightly less directly.  For instance, ending a talk with something like "Please come talk to me if you're interested in X [or, by implication, me].  I would be very interested to take these things further."  Then, when someone talks to you one-on-one or in a small group, you can work the fact that you're on the market organically into the conversation.
Whenever you go to a conference and you're on the job market, concentrate on making meaningful academic and social connections with people, especially potential employers.  Don't prioritize selling yourself directly.  If they meet you and like you and your work, you can follow up later by letting them know you're on the market.  And you should.


What is the impact of gaps on CV in academic job search? I have recently been offered an industry position in the US (we live on another continent).  My wife is a an academic, with a position roughly equivalent to tenure-track asst professor in the US system. If we move to the US, she would likely take 1-2 years off (she's too busy right now to go on the job market this round).  She has a decent, but not amazing publication record: Google counts 16 publications, 310 citations and an H-index of 9 - this is in applied mathematics.  The publications she has are of fairly high quality, but because of various pressures related to childcare and starting her academic position, she hasn't published much in the last 2-3 years.  Her PhD was at a high quality institution (think, Harvard-like but non-US) with a highly regarded supervisor, she did a postdoc at Harvard.  She also has very good student evaluations.

So my question is - what would the impact of a 2 year gap be on an application to an academic institution, and how should it be addressed?  In answering, I'm most interested in the opinion of people who have been on hiring committees, and I'd like to know whether you're coming from a research institution or a liberal arts college (or something else altogether).

If I saw an application from someone with this history and the cover letter explained why the applicant had taken time off work, I would treat the application as though those two years simply didn't exist.  I think that most search committees would also be willing to give a candidate (especially a female candidate dealing with family issues) that kind of treatment.

As @StrongBad noted in a comment, there's a much bigger issue in that it could be extremely difficult for your spouse to find a tenure track research oriented faculty position in the city where you will be working.  The job market is so competitive that even the most qualified applicants have to apply at lots of schools across the country in order to find a tenure track position.

For what its worth I've served on many search committees in applied mathematics over the years.  My institution is a lesser ranked research university.


Why do breakthroughs in science and research that have enormous (and obvious) potential often not (or barely) go beyond the lab? Sometimes advances are made in science research that have enormous and very easily visible potential. There are two examples that I have seen recently.

One example is this newly engineered inexpensive process to convert carbon dioxide into methanol, for fuel. As the article says, "Making methanol from carbon dioxide, the primary contributor to global warming, would both reduce greenhouse gas emissions and provide a substitute for the fossil fuels that create them." This has huge potential, and it seems this should be of great excitement to researchers and activists alike working in this field.

Another example is this development of four strategies that represent partial but significant progress into the fighting of general cancers. This also has lots of potential in its field, and clearly is more advanced than many methods used today.

These two advances are by no means the only ones of such significance that I have seen, and there are surely much more that I have not seen, yet none of us see these go beyond this stage.

Why do breakthroughs in science and research that have enormous (and obvious) potential often not (or barely) go beyond the lab, and what makes them different from those that do become applied in the real world?

They are just flashy press releases.  They're really not as cheap (including both capex and opex), easy, high yield, resistant to catalyst impurities, or scale-up able as written.

I've been seeing these flashy press releases on thin film fuel cells and the like (methanol production, energy production, F-T synthesis, biodiesel, switchgrass etc.) since at least the 90s.  The current press releases look amazingly similar to the same press releases from back then, with little evident improvement in the field.  Or realization of (or shame for) the past press releases that went nowhere.  Just a new assistant professor and another press hungry R1 school putting out more sizzle, sans steak.

And don't get me started on "nano".  That was already max hype in the 90s and has basically gone nowhere commercially (i.e. outside the realm of ivory tower careers).  And it still seems like a hot area for NSF funding and the like.  And it's a field that really attracts the ego scientists and self-promoters.


My personal views and my research work kind of collide. What can I do? Climate change and mass extinctions are happening, so in my private life, I try to be as sustainable as possible. For example, I eat almost exclusively organic vegetarian food, I have no car, buy most stuff used, have a renewable energy contract and avoid flying (especially intercontinentally).
But when I look at my professional life, it is a different story: I am doing my PhD at an institute, working on a side branch of microelectronics. Our institute reportedly uses the amount of electricity in a year that is comparable to a city of 25,000 people. While certainly a lot of what we are developing might end up being useful, other developments aren't leaving the prototype stage once a project is finished. We produce a huge amount of electronic waste on the way to success and other not very sustainable stuff is happening along the way, too.
My dilemma is that I really enjoy working there. I love my colleagues, my supervisor is great, the work is challenging, but extremely interesting. I have a lot of freedom and support, and a good career outlook. And: I am really good at what I do. I published papers and I built a great network.
But on the other hand, I really question the need to integrate electronics everywhere. I cannot really stand behind a lot of what I/we do and the unsustainable practices in our industry. To make matters worse, we even sometimes work on projects for the military — which goes totally against my political beliefs — but the projects themselves are interesting and fun to research.
I am really torn between what my personal beliefs are and what I do at my work / during my research. I try to convince myself that what I do is ok because it is better to have critical people working on a topic, that aren't afraid to question the necessity of new developments, than just the ones who blindly think everything is great. But this sometimes feels like I am gaslighting myself into thinking that what I do is great, even though I know in my heart that it is (kind of) wrong.
What are strategies to deal with this, apart from changing careers (because that train has kind of left the station)?

The fact that some projects lead nowhere is natural to research. That is not "wastage", it's investment. If something works, it can be scaled up and benefit humankind. You can try and concentrate on projects where, if successful, it will save energy, optimize water, etc.
I suggest it is legitimate to waste material and energy to develop these to viable directions, because it will save orders of magnitude more than you waste now. So, I do not necessarily see a contradiction in finding good topics that correlate with your lifestyle/philosophy.
As for defense research, that's quite a different matter. If you really do not believe in defense work, there is no way you can justify to yourself to do it. To take the advocatus diaboli: some people may say that it is in the nature of things that there are less than friendly actors around in the world; neutral and even friendly relations can sour to a point where it is essential to have the country you generally agree to live in the military option at least in the hope that its use will not be necessary.
But if you are strictly opposed to this, either as fundamental principle of non-violence, or because you believe that with all lofty aspirations, the military-industrial complex and/or the political echelon has too many ulterior motives, then I do not see a possibility to reconcile your private beliefs with defense-related work.
The argument of people effectively supporting an activity they fundamentally disagree with by offering a "critical" perspective is not something you can easily get away with, unless you live in a dictatorship and have no reasonably good other choices. And there, being "critical" means being subversive.
To use a similar picture: If you like the taste of steaks and want to protect animals, there is a contradiction; unfortunately, that means that the best you can do at this point are vegan steak replacements. The ones I know are so yucky that it's better to identify genuinely vegan food that does not pretend it's something else.
Standing up to principles requires sacrifice - not always, but more often than not.


Are both GRE and TOEFL a must for a PhD program in the US? Or only TOEFL will suffice? I have completed my masters in English Literature from India and now want to do my PhD from the US. Should I take both the tests, i.e. TOEFL and GRE? Or only TOEFL will be enough? And if I need to take the GRE, then is it a must to take the subject test as well?

The TOEFL and the GRE are quite different. The 'point' -- in as much as standardized tests have one -- of the TOEFL is to figure out if you have sufficient mastery of English to study (and potentially be a Teaching Assistant/Instructor) in an English-speaking environment. The TOEFL is generally required of all non-native English speakers. Graduate programs often waive this requirement if the student is enrolled in and about to complete a degree from a US institution (or did so recently). It's possible that they might waive the requirement for a similar student from a university in other English-speaking countries such as the UK, Australia/NZ, etc. but I am not sure (I don't know anyone who has tried.) Sometimes graduate programs will state that they will waive the TOEFL requirement for a student from a bachelors/masters program with English as a language of instruction. Most Indian colleges/universities I know of are English-medium, but I've never heard of an Indian student applying from India who has had the TOEFL requirement waived this way. (I am an international student from India. I was an undergraduate in the US, and the TOEFL requirement was waived for me by all the graduate programs I applied to.)

The GRE on the other hand is generally required for all applicants, domestic and international. Wikipedia says that it aims to measure ''verbal reasoning, quantitative reasoning, analytical writing, and critical thinking skills that have been acquired over a long period of time and that are not related to any specific field of study.''

The TOEFL is more of a checkbox - programs just want all their international students to have some minimal mastery of English so they can perform the usual duties of a graduate student. The GRE is more of an actual comparative tool for all the candidates which can actually affect an admission decision. The two tests are quite different in content and questions --- the TOEFL might contain a question which will play an audio clip of a 'usual' conversation between a student and a professor and ask you whether you can understand what was said; whereas GRE questions might ask you to critique the logic of an argument or perform some high-school level problem-solving task.

Notably the TOEFL obviously does not have a quantitative section whereas the GRE does.

At this point in the US, I believe there are very few graduate programs that do not require at least the general GRE of all its applicants. Subject tests are not as widely required, but in my experience the better programs require them.

tl;dr: you will probably need to take both the GRE (general) and the TOEFL. Look into the programs you want to apply to to figure out whether you should take the GRE subject test.


Cost/Benefit of transfering from Masters to PhD mid-program I am about 8 months into a Masters program. My advisor just asked me to consider transferring into a PhD program with him. If accepted, the work I have done towards my Masters would count towards the PhD. Also he said that if I struggle in the PhD it would still be possible to revert back to the Masters, so there seems to be very little risk.

My end goal was always to work towards a PhD and often felt overwhelmed by the length of time and resources it would take. So this opportunity seems too good to be true? I get to skip 2 years of work and fast track towards my goal of PhD? Is it common to skip the Masters, or is this opportunity actually quiet valuable? I want to have a proper view of the costs and the benefits before making any decisions, so I am looking for views I am not considering.

As an example of a perceived cost, I don't consider the institution I am at to be particularly well known/quality and assumed my choice of institution for my PhD would be much more important and I would go for more prestige. Is this a valid concern?

Whether it is "okay" to skip the Masters depends on your country and field. In US institutions in most scientific fields it is considered the norm to go directly from Bachelor's to PhD (of course many people get a MS along the way, too, but it's not necessary). In my experience, though, even in countries such as China where enrollment in a MS program is generally required to get accepted to a PhD program, once you get your terminal degree, nobody cares too much about what came beforehand.

Regarding whether it's worth your time, this depends on what kind of offer you're considering. Is the advisor strong in their field (regardless of the prestige of your institution overall)? Could you expect to publish two or more "good" publications ("good" is a relative bar, of course)?

In general, my feeling has always been that as a student, it is easy to "trade up," i.e. go to a MS program stronger than your BS program. Do a postdoc somewhere stronger than your PhD institution. However, once you leave the student/trainee portion of your career, in academia you are more likely to have to "trade down." The MIT and Stanford grads are enough to take most of the top faculty positions in any field in a given year. Therefore, if you want an academic career, you may put more emphasis on prestige.

If you aim for corporate work after graduation, then being at a "better" institution will probably open you up to a better network of alumni who could help you navigate to a good entry level job that fits your skills, setting you up better for future success.

Trading those away would be worthwhile if you really felt that this position was (a) likely to give you academic output that could compensate for the potential future networking loss, or (b) if you felt like this was the best offer you were going to get, or were unsure about the possibility of future offers (i.e. a bird in the hand is worth two in the bush).


Exploring something completely new, even silly. As it were, I finally dreamed up a direction to explore and write something about, but just like every other time, I noticed someone senior has written has already explored the line of thought. In Physics, it seems as if there is practically nothing novel that I can explore. I know in part that may be it is just that I am not able to think fast enough.

If I am thinking about exploring something, and realize someone has already explored it, then decide to completely independently explore it without reading their work, and arrive at something similar or different, is this something that I can still put out there with my name? The thing is I have not written anything yet and have no reputation, and so I am hoping that I can put something may be simple but hopefully different and a bit new, even if it is silly. The problem is that as far as I can tell and after trying for a bit, it is looking like the chances of me doing something not completely explored might be even lower than I thought.

I am a serious scholar though not in any program at the moment, and have quite a bit of time. Any suggestions as to what the right approach is would be great.

What you are writing is pretty much exactly the reason why non-senior researchers in academia have advisors:

It is very hard to have a complete overview over a field, including which lines of thought have been explored, and where the open problems are. Many open problems are known to researchers in the field without this being obvious from the papers.
In order to perform meaningful research in a field that is not brand-new, the best way to get started is to focus on something small but interesting. Developing a completely new theory in Physics is, for example, probably not the right way to start. Coming up with research questions that are small enough so that they are unexplored yet interesting enough for meaningful research is not trivial and requires substantial experience.
To some extent, starting with research also means to refrain from too high expectations of what can be achieved. For example, as a computer scientist, you could reduce an aim to "build a 100% safe self-driving car" to "detecting a dog on a leash next to pedestrians that may jump onto the street from image data only". The latter is more achievable, yet fits into the big picture of building a self-driving car.

So my advise would be to get someone who is at the frontier of the field that you are interested in and who can give you guidance.


Do I need to make a Bachelor's thesis to apply to a master's degree from a top 100 University in the world? So let me tell you my story: I studied Industrial and Systems Engineering in the 24th best university in my country, which is around number 1683 in the world. I am from Mexico and thankfully our government is eager to pay our full tuition if we are accepted to one of the best 200 Universities in the World to study a Master's Degree, mainly in engineering.

My main challenge at this moment is to be accepted in a masters' program at an International University. My grades are good, I have a "promedio" of 92, which is pretty high, although not the highest of my class, and I had outstanding results in the professional test. Because of these results, I was exempted of having to do a bachelor's thesis.

Should I sacrifice my opportunity of earning working experience to dedicate myself to make a good Thesis? I am eager to do so if I really need to.

Thanks a lot to the people that took the time to read this.

I've never been on an (academic) admissions committee, but I can hardly imagine that, instead of reading the title of still another bachelor thesis, I find "exempted by university from writing bachelor thesis because of my course performance near the top of class," and consider that a negative. So I don't think you should sweat that. In most fields, undergraduates are not expected to have done research prior to taking up graduate studies; exceptions, as I have learned here, include math where this appears to be more commonplace. For some of your target fields (eg, supply chain management), I know it isn't, or at least wasn't, as some of my research was arguably on the most theoretical end of it, but I had no relevant experience with it prior to writing my paper; but that could have changed. I don't know about the others.

In terms of being accepted at a good school, the ranking of your alma mater is probably what I would worry about most. You'll need strong letters, ideally from people known internationally (check their publications; ask them). You should definitely approach someone from your U.S. experience for a letter.

Finally, your first, second, and last question (as well as part one of your question 4) are highly personal decisions that you are unlikely to get feedback on here. They are off-topic, and even if they weren't, I wouldn't ask such questions on an anonymous internet forum. Ask friends, family, any mentor, and yourself.


Why does research on the busy beaver function get funded? I saw this video about the busy beaver function and looked at the applications section of the Wikipedia article about the busy beaver function.

I concluded that there is zero practical or even theoretical value in searching these numbers S(n) : n > 5. Now in the video the person says, that people are using supercomputers to calculate S(5). Now I assume using supercomputers is expensive and so somebody is obviously funding this research.

Am I too narrow minded to see the value of funding such research and there is actual value to it or what other reasons are there to fund such endeavors? I assume most academic research is funded by taxes and so it should bring some value to society. While there might be some intellectual value in having such knowledge, does the cost justify it?

PS: Probably this question is too broad/opinion based, but I didn't know on what SE site it would fit best or how to improve it to make it less opinion based, feel free to edit.

EDIT: The economic question is partially solved, by supercomputers being much cheaper than I thought so that argument falls flat. Still could somebody give me a hint on what the value of such research is?

Extremely likely, this concrete example is useless. So is any other concrete example. However, there are many of these examples, and we do not know which handful of them will be useful.

Some ancient people wasted time on research in number theory, some of which ended up being practically useful. Some studied motion of stars in detail far exceeding calendaring and navigational needs. This also proved useful.

Many, many studies also led to dead-ends. At the time, one could not tell which of them would.


How to choose good journal in neuroscience with no fee? I'm a post-doc but with some other post-doc from other labs we are doing research in neurosciences in our free time, and so we are not funding for these projects.
But we would like to publish in a recognized "serious" journal (but no need for a high impact factor).
We tried to look at https://doaj.org/ but we do not know on which criterion to sort the thousands of journals listed or decide if it is a serious journal or not.
On which criteria can we consider a journal being "serious"?
Thanks a lot!

I am not at all familiar with the landscape of journals in neurosciences, so I cannot give you a concrete journal name. But consider this: you are writing a paper to be submitted to a neurosciences journal. I presume that this paper includes references to related work, also published in similar journals. Which journals appear most often in your bibliography? Consider those for your publication first.
Alternatively, seek out the publication records of neurosciences researchers that you truly admire, and find out which journals appear frequently on their (recent) publication records. If top neurosciences researchers publish frequently in a journal, it is likely to be a recognized journal accepting neurosciences papers.


Do Master of Research programs require prior research experience? I have a research question that I want to explore, so am considering completing a Master of Research (MRes) degree in the UK. I will soon finish a terminal master's program by coursework (in the US), with no thesis option, so I do not have a research background. Is research experience a typical prerequisite for an MRes?

No, you do not need research experience to get on a UK MRes progamme. Though it will help.

It may vary by institution; this answer is based on the one MRes from my subject area.

The UCL MRes in Energy Demand Studies has the following prerequisites (taken from that linked page):

  Good numeracy and literacy and an ability to present ideas clearly
  An appreciation of the importance of behaviour and society in energy demand
  An understanding of or ability to learn basic physics and engineering concepts
  Strong performance in a science, engineering or social sciences discipline

The course, in its first half, contains a lot of taught material. This covers, amongst other things, research skills.

Having research skills, or a research track record, will help you with getting onto the more competitive courses, such as this one; and it will improve your chances of getting your study funded too.

One of the selection criteria is (ibid):

  how your academic and professional background meets the demands of this challenging programme

so if you can show a research track record, that will help fulfill that criterion.