Disclaimer: These are based on statistics and information I have collected, as well as subjective opinions from those who have gone to grad school, and my own experience applying to graduate programs in CS in Canada. I will probably make amendments to this article as I progress through graduate studies and develop more opinions. :)
Reading about graduate studies on the internet (particularly on places like /r/uwaterloo) can sometimes be frustrating, because there is a lot of misinformation. Many of these are based on horror stories that are passed on from pop culture, TV tropes, family friends, and rumours that your friends tell you. I wrote this article in hopes that I can bust some common myths, so you can make a somewhat more objective decision on whether graduate studies is the right choice for you.
One thing I had to realize was that the nature of graduate school depends on heavily on the country you do graduate studies in, as well as your specific research field. As such, I will specifically write about graduate studies in Canada, specifically in Computer Science, along with how it compares to some other countries like the United States. I also focus specifically on research-based programs.
Also, I want to emphasize that most of these myths exist because there is a degree of truth to them; these cultural fads and tropes do come from somewhere, and reflect someone’s honest experiences. My purpose isn’t to try to downplay or disregard that. That being said, I want to provide enough context to these half-truths to try to tell a more comprehensive story.
Myth #1: Graduate school is X more years of paying tuition
This is true, but doesn’t tell the whole story. In Canada, both research-based Masters programs as well as a PhD programs are fully-funded, meaning the the school pays you in the form of a research assistantship (RA) as well as a teaching assistantship (TA). Usually, this is a significant sum of money that covers both your tuition as well as living costs. Some schools guarantee both the RA and TA funding, and most others guarantee at least TA funding. Graduate school is essentially a job where you get paid to do research and help teach courses, while also taking courses yourself and getting a degree.
This in contrast to a coursework-based program, where funding is not guaranteed. These are usually considered a professional program, in that most people get a coursework-based Masters degree for career reasons. These are similar to the “cash-cow” programs in the United States, where schools charge astronomical amounts of tuition in exchange for advanced training, a degree, and a better chance at the visa lottery. Funding for a coursework-based program can come from RA/TA work similar to a research-based program.
The purpose of a research-based program is fundamentally different from that of a coursework-based program. Research-based programs (both MSc and PhD) are meant to train the next generation of researchers, whether they end up in industry or academia. As such, students in research-based programs are expected to conduct research, publish papers, present to the wider community, and eventually write and defend a thesis / dissertation to graduate. Coursework-based programs in comparison have little to no research focus, and students can graduate after taking a set of courses.
Below, I will list out the Canadian schools I received detailed funding offers from, as an international student:
There are some important distinctions to make:
- McGill offered the McGill Engineering Doctorate Award, which guarantees $37,000 CAD in funding.
- Waterloo offered the David R. Cheriton Graduate Scholarship as well as the International Master’s Award of Excellence (IMAE), which tops-up the base RA funding by $17,500 CAD.
- Toronto guarantees both RA and TA funding, and the TA-ship only expects around 2 terms of commitment, whereas Waterloo expects 3 terms.
- UBC guarantees around ~$20,000 CAD in funding per year (for MSc) through a mix of RA / TA-ships, but students can usually undertake additional RA / TA-ships for more funding. For PhD students, the tuition is waived.
Personally, I felt that Toronto and McGill were both the strongest in terms of RA funding, mostly because they were both guaranteed. Toronto offered around ~$3000 more after tuition, but that gets roughly canceled out by the difference in cost of living between Montreal and Toronto.
It’s interesting to note the hourly pay & expected hours for the respective graduate schools as well. Waterloo offers $34 / hour at 160hours / term, Toronto offers $48.1 / hour at 60 hours / term, McGill offers $29.3 / hour at 100 hours / term, and UBC offers $31.85 / hour at 192 hours / term.
A note on funding… schools in the United States usually tend to pay slightly more in terms of the total compensation. However, this usually comes with a catch because most funding comes in the form of a grant, often from the government. This means that you are often tied to a specific project by virtue of being funded by a grant. This is unless you already have an external fellowship (such as the NSF-GRFP or Hertz Graduate Fellowship for US citizens, and Funai Foundation Scholars for Japanese citizens, etc) that pays for your living costs and tuition, but these fellowships are very competitive. I didn’t end up applying to any of these fellowships since I decided to apply to graduate school last minute, but definitely apply to these if you are thinking about doing a PhD in the US- even just applying for them looks really good on applications.
Canadian schools, in contrast, usually have a minimum guaranteed amount of funding that the school / department itself guarantees. This reduces a lot of stress on students, especially in uncertain times like the post COVID-19 economic depression, where a lot of grants and funds could be jeopardized. It also makes students less vulnerable to abuse and coercion. Research-based masters programs in Canada are a really good deal — you get to do a 2 year long mini PhD to try and see if you like research, whilst getting a degree and being paid!
Myth #2: You’ll be extremely poor in graduate school
Building off of Myth #1, this is another one that holds a good amount of truth. Yes, it’s true that you certainly won’t be making as much as you would if you went straight into industry, and the stipend is just barely enough to cover for tuition and living expenses.
That being said, these funding amounts are the minimum funding amounts, implying that there are usually ways to increase the funding:
- Scholarships and Fellowships
Each school usually has a big list of scholarships and fellowships that you can apply to, which all add to your base funding amount. In Canada, examples include NSERC, Vanier Canada Graduate Scholarships, Ontario Graduate Scholarships, and many more curated by the school you might end up going to.
In Computer Science, there is also a wide array of external industry fellowships that you can apply to. Some of these fellowships include the Google Fellowship (full tuition & stipend), Facebook Fellowship (full tuition & $42,000 USD / year), Adobe Fellowship ($10,000 USD), NVIDIA Fellowship ($50,000 USD), Microsoft Fellowship (full tuition & $42,000 USD / year), Ada Lovelace Fellowship (full tuition & $42,000 USD / year), IBM Fellowship ($35,000 USD / year), and probably many more. These fellowships are very competitive, but are lucrative and look great on academic CVs.
Industry internships as a PhD student in Computer Science pay very well. Most large tech companies have research labs that hire PhD interns to do research, and the top-end of these internships can pay up to $10,000 USD / month. Over 3–4 months, you can make close to $30,000 to $40,000 USD, which serves as a direct top-up to your funding. Do note that your stipend will often decrease if you are not working on school-related research over the summer, so check what the policies are at your prospective school. Some advisors also do not look favourably upon doing industry internships, so you should also check with your potential advisor before committing. If you are an international student, work authorization is a factor to consider; getting a work-permit as an international PhD student can sometimes be non-trivial.
3. Industry PhD
This is a route that is rare, but seems to be increasingly common. Many advisors at universities have collaborators in industry, or are cross-employed by both university and industry. Students who study under such professors often work with the industry partner, leading to increased earnings as well as access to resources (compute, datasets, domain expertise, etc). This is a good option for someone who is interested in both industry and academia.
There are potential downsides to this. By being employed by industry, the scope of your research projects can be limited by the company’s needs. You may also end up having less flexibility due to NDAs and less mobility due to visa regulations. Ben Recht, David Forsyth, and Alexei Efros wrote a famous critique on this cross-affiliation model, listing some of the main drawbacks.
Some companies that follow this model in one way or another in Canada include: Google, Facebook, SideFX, NVIDIA, Uber ATG, Ubisoft LaForge, Epic Research, Borealis.ai, Element.ai, Autodesk, and probably many more. Often times these connections to industry come from the students themselves rather than the advisors, so definitely seek out for these options if you are already in industry. Certain schools like the Montreal Insitute of Learning Algorithms (MILA) also seem to advocate this sort of model more; MILA has a facility housing many industry partners who employ many MILA students part-time.
Note that once you graduate from a PhD program, the possibilities are endless… whether you stay in academia as a post-doc / professor, go into industry as a researcher / advanced engineer, start your own company, or go into an entirely different field, both the technical skills and the communication skills you develop as a CS PhD student prepare you to solve any problems that you might face.
Myth #3: You need good grades to get into graduate school
Having good grades won’t hurt, but isn’t a golden ticket into graduate school. Conversely, having bad grades won’t always prevent you from getting into graduate school. Prof. Kayvon Fatahalian has a great set of slides that discusses this in detail.
Graduate admission committees for research-based programs(generally) evaluate your application package holistically, with a focus on research experience. This can be evaluated in a number of ways, including your CV, letters of recommendations, publications, and your statement of purpose. Having participated in research as an undergrad (and a letter of recommendation to evidence this) is a strong indication that you enjoy research and have good expectations about research. Having publications (especially first-author publications at major conferences) helps even more because they evidence your ability to execute at the level of a graduate student.
Another underrated aspect of the admission process is connections. The admissions committee and your potential advisors are humans, so they will inevitably have a bias for candidates with strong letters from professors they know well. They will have an even stronger bias for applications from students whom they know personally. Given this, it’s important to put yourself out there whether that means publicly advertising your research works, giving talks, or writing blog articles. Volunteering at / attending conferences and research internships (both in academia and industry) are other ways to get to know people. Getting to know professors, doing great work with them, and publishing is usually the golden path into graduate school.
In Canada, a good baseline for graduate studies seems to be a 80% cumulative average or higher, mostly because many schools and scholarship set the cutoff at 80%. That being said, I know many students who were accepted with lower averages, especially for masters programs. Don’t let grades get in the way of pursuing research!
Personally, I had around a mid 80s average, which professors have told me is not exactly the most competitive GPA. Thankfully, I still got accepted to great schools for research-based masters / PhD programs, probably thanks to some combination of luck, publications, and connections.
Keep in mind though that the above usually doesn’t apply for coursework-based programs, where they do care about grades (and GRE for US schools) more, since the emphasis is not on research. The most competitive masters programs however do tend to still look at research experience, because of the overwhelming number of applicants with good grades / test scores.
Myth #4: Social prestige is the primary metric for picking schools
Generally speaking, one of the primary metrics for picking schools should be whether the school has professors that you would be interested in working with, not social prestige or general rankings. Social prestige is a weak metric when it comes to research, because what specific research communities think is often different from what society thinks as a whole.
One heuristic you can use is csrankings.com, which ranks schools by the number of papers accepted to the top conference in the respective field. By clicking on the individual schools, you can see the professors alongside their publication count in the given field. It’s important to be aware of both the pros and cons of this metric:
- Can find professors who are actively publishing in your field of interest. Publication count is a crude metric, but the reality is that you need to publish in order to go to conferences (where you can meet people and practice presenting your work to the community) and ultimately graduate.
- Can find schools with a large number of faculty who are working in your field of interest, which opens up room for collaborations and new perspectives.
- The ranking omits many professors who are not in the Computer Science department (e.g. ones in related fields like Math or Engineering) as well as professors who share time with industry.
- The conferences the ranking considers to be ‘top conferences’ may not align well with your specific research interests.
- The overall ranking has a heavy bias for fields with high publication counts, such as machine learning and artificial intelligence.
Of course, the final decision should be a holistic one… there are many metrics like culture, location, environment, etc that heavily affect your overall productivity and happiness. Your potential advisor’s advising style, the overall research atmosphere at the school, and the other grad students you will interact with are also very important factors. These factors are much more personal though and will probably require some soul searching to determine.
Exceptions apply when you are not sure about what research field you’re interested in, or you’re unsure whether you want to even stay in research. In these cases, going to a school with higher name value (and overall stronger across all research fields) may make sense, to diversify risk.
One thing to note though is that in Computer Science graduate programs in Canada, students are generally accepted to an advisor, not to the department, so switching advisors may not be entirely trivial. Some schools in the United States admit specifically to the department and then have a rotational program where students rotate between advisors and labs, which could be a good option for students who are unsure.
Overall, I firmly believe that Canadian graduate schools provide an ideal environment where students who are interested in Computer Science research can thrive. Research is a very fun endeavor, and graduate school is a once in a lifetime opportunity to have the freedom to do really cool stuff in the bleeding edge of technology and science. I hope that this article can provide some additional insight into CS graduate studies in Canada… and if you think that there are things I missed / errors in the article, please let me know! I plan to maintain and update this article throughout my graduate studies.
There’s also a lot of resources on the web about CS PhDs (but not many sources that specifically focuses on Canada). I will link some of my favourite ones:
Do Grades Matter? by Kayvon Fatahalian
The Ph.D. Grind by Phillip Guo
Grad School Resources by Kalpesh Krishna
Also, special thanks to Ivana Kajić, Steven Feng, Alla Sheffer for their inputs on the article!
Follow me on Twitter for more content: @yongyuanxi