TakeruK

I would have thought a full year return in California would go further for establishing California residency than a partial year return? At the same time, when filling out my return this weekend (had to do it for the US, California and Canada, what fun!) I remembered that you could only file as a resident of a state if you spent more than 6 months in that state? And I think you can amend/correct a return (see http://www.irs.gov/uac/Nine-Facts-on-filing-an-Amended-Return).That website says you can do so within 3 years (but you probably have to wait until they process your first return). Although at this point, perhaps the best thing to do is to first ask the grad division to confirm whether or not a full year return is good enough? That is, perhaps that email was to warn people away from filing a full year return in their old state (i.e. recommending that you file partial year in California and partial year in old state), in which case, filing a full year return in California is even better.

This is a good point -- I was focussing on whether people thought the research work itself was "valid" or not. But there is definitely tension when it comes to things like resources and funding. For example, when I was involved in the TA labour union at my previous graduate school, I learned a lot about the differences in the way graduate students are treated in the social sciences and humanities. Although we (the Union) always tried to present the need for unionization as a way to raise our colleagues in less-funded fields to get fair/equitable treatment as the natural science students, one of the biggest hurdles we had to overcome (in addition to the University) was convincing the natural science students to be on board. At best, many natural science students felt that since they did not benefit from the union, they were not interested and at worst, many natural science students feared losing their benefits because they felt funding was a zero-sum game (not necessarily true). So I experienced a lot of natural science vs. social science/humanities animosity during my time as a union representative for a natural science department. I think this is too bad, because the real enemy is the people making the decision to poorly fund all of us, and not each other. Also, a lot of what the union was fighting for was not even financial and not things that would take away from the natural sciences (for example, laying out a fair and transparent protocol for assigning TAships, which apparently was a big problem in one of the social science/humanities department in particular). That is, there were battles but not because the natural scientists didn't believe/trust the research of the social sciences/humanities, but because in a world where resources are scarce, a lot of people just want to look out for themselves. There were even some battles between engineering/applied sciences and "pure" (not meant to indicate any type of superiority, just "not-applied") sciences as in general, the engineering/applied fields were even better funded than the pure natural sciences.

Note: I'm not directly contradicting Cosmojo's advice--if both programs are indeed "Tier 1 research" places, then I don't think ranking differences between such schools are very important. However, from your example, I do think that the two schools are on two separate tiers. But I'm not intimately familiar with rankings in your field--I can only say that in my field, there is a huge difference between rank 20-something and rank 80-something.

I don't think "big fish in a small pond" is always good. There are certainly some advantages, as if you are the most promising graduate, the profs will spend extra time in helping you network and succeed. However, if you are middle-of-the-pack in the small pond, then you won't get as many advantages as the biggest fish would. On the other hand, if you are middle-of-the-pack in the big pond, the "brand name" power of the big pond is still an advantage. People are going to take another look at you when they see your school name on top of your CV, in a list of abstracts, and on your name badge at conferences. I've been in both types of schools and I can say that although the work I'm doing at both schools are about the same quality, I'm getting a lot more recognition for it at my "brand name" program. I think the best way to find placement records is to find the names of all graduated students in the last 4-5 years. Then, do a search on them to see where they are now and also search for their papers to see how good their work is / how widely cited they are. In order to find the names of recent graduates, here are some options (somewhat ordered from easiest to hardest) 1. LinkedIn and other networking sites 2. Department website often shows this, listed by year of entry or graduation 3. Go on each of your POI's research websites and look for their published list of graduated students (sometimes it is directly on the website, sometimes they are listed in the CV) 4. Look up all the papers published by your POIs in the last 5-10 years, and note who their coauthors are. In my field, the author list usually goes Student, Supervisor, Collaborators, so I'd look for all papers where the professor is the 2nd or 3rd author and check if the 1st author is also from the same university. Note down all such names and then find these people's research websites to find their CV to see if they were indeed a graduate student there and to see where they are now.

Well, these career development events are often hosted by US institutions and so these institutions know a lot about non-US graduates doing postdocs at US schools, but they would not know very much about US students going abroad! I think this is something that would generally be the responsibility of the foreign schools to send information to inform/recruit US graduates to their programs! I agree that this would be a useful resource to have on campus though. Students at my school are currently trying to reach out to our alumni network to have recent graduates come tell current students about their paths (whether it's a US postdoc, international postdoc, US industry job, international industry job, government organization, NGO, etc.) so that we can learn from their experience. Unfortunately, for logistical reasons, getting our alumni that went abroad after graduation is the hardest since they are so far away! I agree with you that it is a "bias" but I think it's a bias due to the circumstances above, not a deliberate attempt to hide information from US students thinking of doing a postdoc in another country! I would recommend that if you want to know more, you can find other like-minded students and try to get a discussion panel or some kind of event with this information for you! Maybe at the next career event, or just a standalone event. If you have problems finding alumni who are still abroad, maybe you can find some young/new faculty who recently did a postdoc abroad. Good luck!

I have found that these distinctions or "rivalries" are petty and mostly seen in undergrad. I admit that when I was a first year college student, I also thought my discipline was more rigorous and somehow "better" than the soft sciences or humanities. But that was when I was dumber, more naive and well, looking for a way to "belong" and for better or for worse, one of the ways we try to identify a group we "belong" to, is to identify the "other". As I matured through undergrad, these rivalries diminished and I rarely see this come up as a graduate student ever. I would say that graduate students in the social sciences/humanities and natural sciences all have a lot more in common than they do differences. Through working on cross-campus committees and student groups (e.g. student government, labour unions etc.) I now have a view that our disciplines is just something we do, it doesn't define who we are. There's no point, in my opinion, to compare the "usefulness" of our work, because we all have different ways we contribute to advancing human knowledge. If it helps you to know, there is even sub-discipline "rivalries" within the natural sciences. Some physicists do not consider astronomers as "real" physicists even though we have the same undergrad training. Or some will consider our work as "not useful" because they view astronomy like "stamp collecting", that is, we don't come up with new fundamental ideas about physics, but instead just finding ways to apply what the "real physicists" learn to the universe we observe. Sometimes this is just petty and annoying but it can actually have harmful consequences. For example, some astronomy graduate programs that are in a Physics department will require the grad students to take advanced and difficult Physics courses that have nothing to do with our research just because they think we should do some "real Physics" before we can get a PhD. It's a waste of people's time and I know some great astronomers that end up having a hard time passing Quantum Field Theory and delaying their graduation date by a year or two for no good reason!! Finally, just to comment on your statistical example, I do have a pet peeve: I dislike it when people say something like "I have scientifically, definitively found something to be true" because I don't think these statements are possible. Instead, I prefer the framework of something like "We found that crime at a park is not randomly distributed with 95% certainty" or something to that effect. I would also consider myself a "Bayesian statistician" and in the Bayesian framework, we don't think of things are "true" or "not true" but rather "the probability distribution of X is ...." In particular, if I want to pick stereotypes, I find that the social sciences tend to use arbitrary cutoffs like "p < 0.05" to signal "truth" and that these arbitrary cutoffs are just that -- arbitrary. And, I think sometimes people consider something like p=0.07 as "proving the null hypothesis" but really, a p=0.07 still means the null hypothesis is unlikely, but just not unlikely enough to meet an arbitrary standard for rejection. Instead, I would prefer to see researchers (in the natural sciences, social sciences, and humanities) to report their results as they are (e.g. p=0.07, or the odds ratio of hypothesis A over hypothesis B is X) rather than relying on arbitrary cutoffs. However, while I wrote about my philosophy of science in the last paragraph above, I do not mean to say it is the only correct way to think about statistics. In fact, I recognize that there can be more than one interpretation of statistics/data and that I do not think there is necessarily only one correct way to do it. I obviously have my preference, but I am aware that my preference might not be the best (or only) correct way

I am not 100% certain, but in general, as an American, you will have to pay tax to the United States on all your income, no matter the source. As the above poster said though, you will not be taxed by Canada on the non-employment (i.e. the parts that is not TA/RA) part of your income. However, the tax on $13,000 per year in America will be very small! Also, the Canadian tax rate are pretty low, especially for low income students so you will probably not have to pay any tax at all in Canada. You might even get extra credits/rebate. (You get $400 in non-refundable educational tax credits per month of full time studies, plus $1 for every $1 spent on tuition).

Oops you are correct, OPT is only available after degree completion. CPT is the program to use for work during degree progress. I am on J-1 status and when I was writing that, I mixed up F-1 OPT with J-1 "AT" (academic training) which allows 18 months prior to degree and up to 36 months after degree. Sorry to the OP and anyone else who was confused!

Indeed, python is a lot slower. Luckily, I don't run that many things that rely on computational speed, except for very long Markov Chain Monte Carlo runs, but I just let those run overnight or over a weekend. Also, a down side of python is that it is fairly easily to accidentally do something very slow when there's a much faster way. I try to estimate how long the code will take to run and determine if it's worth optimizing. There has been a few cases where the original way I wrote it would have taken 2 months to run, but through some logic changes, it went down to 2 hours. Python has a ton of Bayesian statistical modules built in now--"emcee" is a great MCMC package that I and others in my field use all the time! I also like being able to use "Quantity" objects that have units built into them. Astronomy uses a lot of different, non-SI units, so this way, I can just store quantities in their natural units, do the math, and then convert to whatever standard unit in the end. I'm sure this functionality is possible in all the languages, but there are packages built by astronomers for astronomers in python (prior to python, there is/was an extensive library of IDL routines--it seemed like astronomers chose IDL over MATLAB decades ago, for some reason). The point of that long story is that you also want to learn about what your field has used in the past, and what the popular trends are right now, because you want to stick with software that will be supported and used by others in your field. Also, if you want others to use your code then you want to stay mainstream! Right now, in my field, python is the popular choice (IDL is favoured by older scientists still but python is gaining more and more each year).

If this is your first coding language (sounds like it since HTML is not really a coding language and it was awhile ago!) and you have some spare money, I would strongly recommend buying a textbook and learning from it. I think structured learning (whether it's a class or self-taught from a book) is important for the first programming language because then you are introduced to all the important concepts in programming (logic, loops, variable types, objects, etc.). Again, I'm speaking as a non-computer scientist so my focus is more on the applications of coding so maybe a CS student will say something different. I would recommend that you look up your school's introductory programming course to see if they offer one in Python and then follow their assignments and textbook. Or, you can take an online coursera course. There are also a lot of tutorials online too, but you should try to find one that is meant for people learning Python and how to program, not just people who already know other languages and just need to learn Python.

This is super unlikely. However, if it does happen, then they simply accept fewer people next year. In my current program, we accept on average 6-8 people and expect 4-5 to attend. If you look through the past few years, you will see that there are years where only 0-2 people accept, and this is usually followed by a year with 10 or so admittances**. If you trace the history further you will also see that there were two years in a row where 8 people accepted two years in a row--in the following year, we only made 4 offers. This means that your chances of getting into various programs can vary from year to year, based on factors completely out of your control (like the one above) and government funding decisions. **Note: In my program, they will not accept people that they don't think meets their standard, so even if there are years where they want to make 10 offers, if they don't find 10 applicants they think is good enough, then they will just wait another year.

No, the grad schools don't care whether you take courses in the summer or other terms. Also, taking only 3 courses per term near the end is not a big deal either, especially if you do something else with the time. For example, if you are able to do research projects during your final years (or an honours thesis), that will go a very long way towards making you a better applicant for grad school!

I would recommend R or python, but I am not a computer scientist. I do a lot of Bayesian statistical analysis in my research and I love python. My former officemate swears by R. I think these two languages are the best because: 1) They are free. While my school provides me with tons of MATLAB licenses, I can't always count on this in postdocs or future work. I want to be developing code during grad school that I can use forever. 2) They are modern and continually updated, especially with statistical packages!

I'll have to check out Forbidden Island. I agree that Pandemic easily leads to alpha gamers (sometimes I am guilty of that ), especially when there are few experienced players!

I know!! I have an American friend that moved to Canada for grad school (she did her PhD at my undergrad school, I am doing my PhD at her undergrad school) and we both have spouses that need to find work and we compare experiences often. It's completely the opposite -- Canada does so much to let her and her spouse work and we had to jump through so many hoops for us to get the same. Also, the thing I completely do not understand is that foreign student status in the US requires you to have ties to your home country--basically they just want you to come, learn, and leave. Time as a foreign student does not count for any green card/permanent residence/citizenship! Even though tons of taxpayer money (and American donor money) is being spent on training foreign people!! But in Canada, there is a pathway to PR/citizenship for PhD students so that we keep Canadian-trained people (trained with Canadian money!) in Canada. I feel the American way is so weird and backwards.

Yes and no. As a Canadian earning US income, you will first have to file your taxes with the IRS. Then, you file your taxes with the Canada Revenue Agency (CRA). Grad student scholarships/fellowships are not taxable in Canada, so you don't have to report that income. You just report your TA and/or RA income (only employment income) and calculate how much taxes you would owe in Canada. Remember you can take the all the deductions/exemptions as before (i.e. first $11,000 or so is not taxable). Next, any leftover educational tax credits are applied against your Canadian tax owed. You may or may not have some from undergrad. Finally, any taxes you already paid to the IRS are deducted from your Canadian tax owed ("Foreign Income Tax Credit" or something like that). This way you don't get "double-taxed" (but see note below). You will have to file your US taxes before you file Canadian taxes because you will have to include a copy of your US tax form in your Canadian tax package. Now, if you still have Canadian tax owed after all this, then you will have to pay the CRA the rest. It is unlikely that this is the case though since for our income bracket, the American taxes are generally much higher than Canadian taxes. Note about double taxing: Technically you are still double taxed because you have to use up your educational tax credits before your Foreign Income Tax Credit is applied. However, you can still earn more educational tax credits from the tuition you pay to the US school. This is where it really helps to go to a private school, I claim $40k/year for educational tax credit that will eventually create a very nice tax shelter for me when I return to Canada (but if I do a postdoc in the US then this will go away very fast!)

Pandemic is a pretty fun co-op game (I like co-op games too!). For strategy games that are easy/fun to pick up (i.e. good for parties with lots of new to board games people), I have Guillotine, Mille Bournes, and Carcassonne. I like playing (but don't currently own) Ticket to Ride, Settlers of Catan and Tsuro.

I was fortunate that I did not have to choose between programs that were fit vs. being able to pay all the bills. That said, when adjusted for cost of living, I did end up choosing the program that offered me the least money out of my 3 top choices and I currently* do not regret it at all due to all of the other resources (research funding and facilities) for me to be the best scholar I can be. To clarify my (*) above, there were a few months in the first year where I felt a lot of financial stress as my spouse had to go through a lot of paperwork to first get work authorization and then find work. At that point, we still believed we made the right decision, but the financial stress was no fun on our personal or work lives. Part of the problem was that this was pre-ACA plans being available so health care costs were very high for us. Luckily, my school came up with a one-time additional $7000 subsidy to help part of our very large health costs. By the second year, both of us had income and we were able to stop worrying about money on a day-to-day basis. Although I did not know that they would be able to do this for us when I accepted the offer, part of the reason we chose this school was that we felt the administration was very supportive of graduate students! (That is, my point is that if you have enough to pay all the bills, I'd pick the best "fit" over finances. For your case, I don't know exactly what you mean by "struggle to live off the stipend" -- does it mean living frugally or does it mean you'll have to go into debt? I wouldn't personally be willing to go into debt, but living more frugally to go to a better school might be a good "investment", to a certain extent)

Usually, we don't know all of the details of these situations, so it's hard/not useful to guess why. I wouldn't worry about this being "odd" except for the concern fuzzy raised above. For example, perhaps there is a limit on the number of nominations made every X years or some other penalty for nominating a candidate that does not eventually win. Maybe the department knows from past experience that neither you nor the other candidate are a good match for the fellowship. Unless it's the issue that fuzzy mentioned, I think it's important to just move past this. Don't compare yourself to other students (present or past)! The best people in academia get rejected for various things all the time, and it's important to not take it personally and move on.

I agree with MathCat, there's no harm in asking (especially since it sounds like they are practically inviting you to ask). Of course, having another offer helps the DGS make the case for you but perhaps something can still be done without another offer. Never know until you try.

I am not certain, but I am pretty sure these are not deductible expenses, neither in Canada nor the US.

I know that you all must already know this, but individual cases of not experiencing deception does not mean that it doesn't exist. I have seen places that slightly fudge their numbers. For example, many schools cite numbers like "Because everyone gets 2 chances at quals, only 2% of students do not pass!", or "98% of students that pass quals finish their PhD!". These are misleading numbers. When pressed further, they clarified that the first statistic only includes people who failed quals on both attempts, however, many people choose to not even retake quals and leave graduate school--these people are not included at all. Also, there is deception in how the school/department defines success. Sure, a school could say that the job market is lousy for academia and give real stats. However, this is not helpful to the student at all if their graduate program is still centered around training PhDs to become TT professors. That is, a school that says "All of you will get TT jobs" is not that much worse than a school that says "We recognize and inform you that only 10% of you will get TT jobs, however, we will still structure our program requirements to train you in skills that are only useful in a TT job". I think if schools are truly serious about the condition of the job market, they will do more than just inform students. It is irresponsible to train 10 PhDs a year for TT jobs knowing that only one of them will make it. I know my field is doing better in this regard and they are reducing "traditional" PhD requirements that are only useful if you end up on the TT job market. For example, some schools used to require a lot of extra "scholarly" training (i.e. learning things for the sake of breadth/knowledge) and are now dropping these requirements in favour of training their graduates to have more marketable and transferable skills.

For the career section, resources mostly means money to directly support your research needs. It would depend on field. For me, this means money to buy me a new computer if necessary, money to send me to conferences, money to pay for lunch/dinner for students to meet with visiting scholars. Resources can also mean facilities -- access to libraries that have subscriptions to the right journals, facilities like telescope time (my current school owns 25% of one of the biggest telescopes in Hawaii, giving people here a huge advantage over researchers from other places; and my current school will own more of a future telescope than entire countries). Hope that clarifies!

This really depends on school and country. I see you are from Canada too and applied to Canadian schools in the past! For example, I'm not sure if Political Science is the same as Physics, but in my field in Canada, your POI definitely needs to approve you. At UBC, the rule was explicit that the admissions committee will not admit you for a PhD program unless a prof is willing to fund you. At Queen's, there was no admissions committee--all applications are sent to all professors and each prof makes the decision yes/no to accept you. In the US, because it is a direct-PhD program, new students don't usually start on a PhD thesis right away so we don't usually need to have an advisor formally until after a year or two. So, having a prof speak up for you is less important. At my current US program, it is still fairly important because although our first year is department funded, when we are admitted, a professor is assigned to be the backup funder for the 2nd year in case we haven't selected a firm thesis advisor yet. As for LOR choice strategy, I would first choose professors who have direct experience working with me as a scholar (preferably research). After that, if you still have more than 3 options, then I would pick people who might have connections with the schools you are applying to. You can then pick and choose which 3 to send to each school -- no one says you have to submit the same LORs to every single place I had a 4th LOR writer that I only submitted to some schools (I think 3 strong letters is better than 3 strong letter + 1 good letter) and I showed my 4th writer my list of schools and asked where they thought their letter would make a difference (since you might not know all their connections).

In order to rank them, it really depends on what your priorities are in life. It might be hard to know at this stage though, so maybe I'll rank these factors into two different categories and then you can decide your balance. Ranking of career-based factors: 1. Resources available to ensure your success. This is often but not always, correlated with name/prestige. I think this is the number one most important factor for your future career goals. What you actually do in a grad program will have way more influence on your post-degree success of course, however, what you are capable of doing in your grad program is very strongly linked to how much resources are available to graduate students. Another way to think about it is that you don't want to go to a place where the lack of resources limits your ability to be the best you can be. 2. Fit / number of labs you're interested in. I would worry more about personality fit than research topic fit. Your relationship with your lab/PI is correlated with your happiness/productivity. It's much easier to change your own research interests than it is to change your working environment. 3. Prestige of the school in your subfield. This is related to #1 (although usually the amount of resources comes from amount of funding, which is more linked to overall reputation rather than subfield reputation). It's important to take advantage of opportunities to meet top scientists in your field when they visit for seminars, colloquia, etc. If you are at a lowly ranked school, you are not going to be able to attract as many of these visitors. Similarly, you will not attract as many candidates for postdocs and potential collaborators. 4. How established the graduate program is. I don't think this is a very important factor for career reasons. This is because older programs are not necessarily better--lots of old programs have crappy systems and/or policies that are wasteful/annoying/unfair because "that's the way it has always been". New programs can be innovative and have more modern/forward-thinking policies. Perhaps a better factor is "what is the work environment like?". Ranking of personal factors: 1. Financial circumstances. I put this first because financial stresses are one of the worst types of stresses, in my opinion. I've experienced it first hand and from working with student groups on campus, I've learned that this is one of the most common sources of stress/mental health issues for most graduate students. However, I want to clarify that by "financial circumstances", I mean that it is very important that the answer to "Does the stipend provide enough for me to live?" is yes, but I wouldn't consider money above this to be important. That is, if all else being equal, I would not use this factor to give preference to a program that pays $1000/year higher. 2. Preference for location. 3. Resources available for you to achieve work-life balance. Again, this is often closely related to name/prestige. You want to know if any particular resources you might want to use are available. For example, I personally asked questions about parental leave, childcare subsidies, can they help my spouse find a job, etc. Others might be interested in medical leave, personal leave (e.g. if you have relatives you know you might have to take a leave to care for), does the health insurance cover the things you need, are there enough social activities for you etc. --- It's up to you to decide how much to weigh each list. Personally, I weighed them equally. This means that I would not consider any program that did not meet the #1 factor in both lists!