Introduction
I recently spoke with undergrad students in the Bachelor of Health Sciences program at the University of Calgary, which I graduated from in 2014. As a late-stage PhD student, I talked about my experiences in grad school as a MSc and PhD student in Computer Science at the University of Toronto. This let me reflect on what messages would have been most helpful to hear seven years ago (!) when I was applying for graduate studies. These tips are oriented toward people considering graduate programs in computational biology, though they should apply to any research-intensive program in the sciences.
Do you want to go to grad school?
The best predictor of whether you will enjoy graduate student life is whether you enjoy undergraduate research, since your graduate studies will be research-dominated. Through six years of grad school, perhaps only 10% of my time cumulatively has gone to coursework, and that is in a program with comparatively strenuous coursework requirements, relative to colleagues in other graduate programs. Most undergrad programs offer research courses, where you pursue a research project in a lab and are given a grade by the PI, which will give you a taste of research life. I was extremely fortunate in that my undergrad program encouraged research—by demystifying research and supporting students’ applications to research positions, my undergraduate program heavily influenced my trajectory, both during my undergraduate work and afterward. I did research-project courses in my second and third year of undergrad, took paid research positions through all four summers, and did a full-year research topic course to produce an undergraduate thesis, as did most of my peers. In concert, these experiences made me realize how much I enjoyed research, and gave me experience that was an invaluable aid to my graduate applications and the beginning of my grad-student career.
My undergrad research experiences made me realize that I delight in the freedom offered by research, with the ability to select a single problem and focus intensely on solving it. As a graduate researcher, even as you focus on a single problem, you will continue to develop technical skills, with graduate courses forcing you to learn those skills. More valuable to me, however, has been the coursework I’ve pursued informally as a byproduct of my research. I’ve learned new skills from fragments of dozens of courses, both ones I’ve audited at the University of Toronto and ones I’ve stumbled across online from other schools. What pieces I focus on from each course have been dictated by my research. As I’ve progressed deeper into specific research areas, I’ve read papers centred on two themes: Firstly, I’ve drawn on papers from other working on similar problems in computational biology, so I can understand what approaches they took and how I might improve on them. Secondly, I’ve read papers from the machine-learning, statistics, and signal processing literature, so I can understand more esoteric details about particular technical approaches that are relevant to my chosen problem.
The skills I learn are driven by what my research requires; the research directions I pursue are then often informed by what skills I want to learn. This virtuous cycle has been my favourite part of grad school, as it’s granted me enormous freedom to focus on interesting problems in cancer evolution, develop the technical skills to address them, and participate in the wider research ecosystem. Unquestionably, you pay an opportunity cost to attend graduate school. Friends from your undergrad program who enter the workforce instead will, generally, make more money and be more respected than you. But, in exchange, you will be granted a great deal of freedom, and can lay the groundwork for a fantastic career in academia or industry. I have no regrets about doing my PhD.
Tips for applying to grad schools
Suppose you’re interested in going to grad school to pursue research in the sciences. How do you select a graduate program, and how can you maximize your chances of being admitted to it?
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Even if you don’t know whether you want to go, apply if you’re interested. I didn’t figure out until the October of my last year in undergrad that I wanted to go to grad school, by which time it was too late to write the GRE for admission to U.S. schools. Even with the GRE falling out of style such that many grad programs no longer require it, fortune favours the prepared. If you’re entertaining the possibility of grad school, apply early, then wait for the admissions decisions to decide if you actually want to go. The Department of Computer Science at the University of Toronto flies all admitted graduate students to Toronto before they must decide whether to accept admission offers. This can provide critical information about the merits of a program. Through this process, I learned that Computer Science students at the University of Toronto could do an eighteen-month master’s program in Toronto, rather than having to enter directly to the PhD. Unlike other programs that offer the master’s only as a consolation prize to students who drop out of the PhD stream, Toronto’s Computer Science department encouraged students to do the master’s first, with most students following that advice. This made the choice to go to grad school easy—rather than committing five or more years of my life, I was committing only 18 months, and could walk away with a master’s if I desired.
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Keep your mind open about different graduate programs. The University of Toronto became my top grad school choice only after my undergrad PI (“principal investigator”, meaning a professor running a lab) James Wasmuth suggested it to me. Given my interest in developing methods in computational biology, my advisor suggested that Toronto would be an excellent fit for me, given its many labs working in this area. Conversely, he helped me see that comp bio programs at other schools I was considering were more focused on applications of these methods. When I focused on the University of Toronto as my top choice, I planned to apply through the Molecular Genetics department, as they offered a computational biology program that seemed like an ideal fit for me, given my undergraduate bioinformatics degree. But Quaid Morris, whose lab I ended up joining, suggested that I also apply to the Computer Science department, as it would allow me to take courses more relevant to my interests in method development. I applied to and was admitted to both departments, then followed my supervisor’s advice to enter through Computer Science. In hindsight, this was an excellent choice, as the course requirements in Computer Science aligned better with my research, and the degree as a whole has opened new post-PhD career opportunities that I would not have had in other programs. In turn, knowing I would be joining the CS department helped me persevere through some of the tough, theory-laden CS courses I took as options in my last undergraduate semester.
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Talk to PIs at your undergraduate institute about potential programs, especially if they’re newly minted faculty. Early-career PIs were PhD students and postdocs in the recent past, and so can embody the student’s perspective in offering advice about what graduate programs are most likely to suit your interests. My undergraduate PI began began his professorship in Calgary only a couple years before I joined his lab. Before that, he had been a postdoc in Toronto, and so was able to give me a great deal of advice about whether Toronto’s graduate programs were relevant to my research interests, and about the merits of potential graduate PIs. Even if you don’t have a personal connection with a PI at your undergrad institute, she will likely be willing to chat with you about graduate programs—in general, PIs are delighted to see students continuing in academia.
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Reach out to potential graduate PIs before applying. The University of Toronto’s Computer Science program flew all accepted graduate students to Toronto in March, where we met with potential PIs. Four of my five interviews went poorly. One interviewer had just returned to Toronto from a conference on a twelve-hour overnight flight, then went directly to the university from the airport for the graduate interviews, and so was not in what might charitably be described as the ideal interviewing mindset. Another interviewer had somehow received the wrong schedule, and so never appeared for my interview. A third interviewer seemed most interested on grilling me about the shortcomings of my undergraduate thesis research, focusing on minutiae of my project rather than on figuring out whether I would be a good candidate for his lab. Luckily, however, before flying to Toronto, I had an extended interview over Skype with Quaid, who even before my trip was my clear first choice as supervisor. Talking to him helped me settle on Computer Science as the best graduate program in Toronto, and established the outlines of what research topics I could pursue in his lab.
In some graduate programs, the individual PIs have little or no input concerning what students join the graduate programs, and so will decline to meet with you. Typically, in these programs, students choose graduate supervisors only after beginning the program. In other instances, students need an offer of admittance to a lab from a PI before joining the program. Regardless, connecting to a couple potential PIs and leaving a good impression will increase your chance of being accepted. Such discussions also can help you decide whether you want to join a particular graduate program, given how critical the choice of PI is. Even if a PI has no input on whether her affiliated graduate program will accept you, a fifteen-minute meeting with the PI can help you understand whether you would want to attend the program and join the PI’s lab if you were accepted. Given that you’re contemplating committing five or more years of your life to advancing the PI’s research program, you should at some point be able to meet with prospective PIs before accepting a graduate admission offer.
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Talk to grad students in the labs you’re considering. The PIs of labs you’re considering should be willing to put you in contact with their students, who can offer valuable perspectives on what your life would be like as a lab member. Talk to the students about lab culture: Does the PI have reasonable expectations about work-life balance? Does the PI help them connect to other research groups, and support them in working with the students and PIs from those groups in whatever collaborations arise? Were they given good support from their PI as they matured through their graduate studies? Ideally, your supervisor will give you structure early in your graduate work with respect to choosing research topics and establishing how best to pursue them; later, as your technical skills and research perspective develop, you will want more freedom to choose what questions to address and how to approach them, while still getting robust support from your PI when you request it. You should ask the students what aspects of their lab environments they find most challenging or frustrating. Expect some to be candid in telling you outright what aggravates them about their PIs; others will be more discreet, but may nevertheless allude to an answer in what they say or leave unsaid. No PI is perfect, so hope to uncover run-of-the-mill frustrations—e.g., “my PI is harried and doesn’t respond to my questions as fast as I want, and he sometimes misses deadline”—rather than extreme ones.
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Look where a lab’s students are publishing. Particularly in computational biology, it’s lamentably common for graduate students to be relegated to roles as a cheap source of labour, where they are merely providing computational support for lead authors who are driving the primary research messages. This situation is most frequent in labs that do a mix of wet-lab (at-the-bench biology) and dry-lab (computational biology) work; however, I have seen it also in purely computational labs, where students end up doing computational labour for other research groups. If most students in the lab have a plethora of middle-author publications, with no first-author papers to their names, the PI may not be good at helping students establish projects they can lead. Conversely, if senior students have at least one or two first-author publications, particularly in prominent journals, the lab is likely better-run.
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Consider how you want to balance the three factors that play into your graduate program choice:
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The project you’ll be working on. You should have a sense of potential projects before you decide to join a lab. Most PIs have dozens of project ideas bouncing around their heads, since departing graduate students leave many potential research directions unexplored. Do the projects a PI has on offer excite you? Could you envision devoting a year or two of your life to pursuing them? Will those projects allow you to learn the technical skills you want?
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The school and program you’ll be attending. Does the program offer relevant courses that will confer the skills you need to pursue your research interests? Is the department well-regarded elsewhere, such that completing a graduate degree there will open a range of professional opportunities for your next career stage? Beyond the lab you’re considering, are there other labs working on complementary areas with whom you might be able to collaborate? You can get a sense of this last factor by looking for papers where your prospective PI is a senior author alongside other PIs in the same city. If your potential PI has many such papers, you can judge that your potential PI has tapped into a strong collaborative network working on these research topics in the ecosystem that includes her institute and others in the same geographic area. Such collaborations can of course span wider geographic areas, but collaborations in the same ecosystem (e.g., the University of Toronto, affiliated hospitals, and other Toronto research institutes) are particularly likely to help students gain access to worthwhile projects and build their professional networks.
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The PI. Do her current students have favorable things to say about her? Do her students publish first-author papers, demonstrating that the PI can help them select good projects and see them through to completion? Is the PI early in her career or more established? There are merits to both stages. Early-career PIs will have fewer students, and thus more time to devote to mentoring you and advancing your projects. They are also likely focused on the rat race to get tenure, which means they’ll be driven to help you finish your projects and publish. Later-career PIs, by contrast, may have a better professional network for establishing collaborations with others, and more potential projects for you to pursue based on what others who preceded you in the lab left undone. Their larger labs mean, however, that they will likely have less attention to devote to your projects’ success and your professional development.
Oftentimes, prospective grad students consider their project as the dominant factor in lab choice, reasoning that they want to choose the coolest possible project to pursue since they will be living and breathing it for the next several years. But this is unwise, because there are an infinite number of cool problems to pursue. Instead, the PI should be the dominant factor. A good PI will help you develop a fascinating project, based on outstanding problems she wants to solve that will also allow you to mature as a scientist. A bad PI can make you hate any project by providing too little (or too much!) supervision, making unreasonable requests of you, and abusing the power they have over you. Insofar as project choice plays into your decision, you should consider primarily what technical skills the project will help you develop. I have worked on massive, prestigious collaborations that proved deeply unsatisfying, as they did not challenge me technically; other projects, though much less impressive from the exterior, have been more worthwhile because they let me develop new skills.
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Try to pursue undergraduate research and to publish a paper on your work. I published a little tiny baby paper about a BLAST visualization tool that I developed as a byproduct of my undergraduate thesis. Though we didn’t publish my undergrad thesis until much later, the visualization paper was useful for demonstrating to prospective graduate PIs that I had research experience, and that I could take a project to completion. With bioRxiv allowing you to release your research for public consumption without completing the onerous publication process imposed by journals, you can more easily show potential PIs the early fruits of your labors as you’re applying to graduate programs, then focus on fleshing out a full paper for journal submission later. With that said, do not be unduly worried if you have no publications to your name when applying to grad school—finishing enough research to write a paper, and then actually finishing a paper draft before submitting your graduate applications, is extremely tough. We published my first undergraduate paper only several months after I began grad school, and the second several years later. Nevertheless, that first paper likely benefited my applications for graduate funding awards.
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Apply for every scholarship you come across, even before starting your graduate program. You will encounter dozens of graduate scholarships you can apply to through the course of your PhD. In six years of grad school, I have applied for 37. (My reference-letter writers, particularly my undergraduate supervisor, likely feel that it was even more.) My graduate supervisor encouraged me to apply for graduate awards as soon as I had selected his lab, which allowed me to begin graduate school with supplemental funding.
Winning graduate funding awards allowed me to be financially secure though grad school, meaning I could focus on research without worrying about money, for which I’m enormously grateful. Graduate programs differ in how they handle external funding. A colleague at another school has essentially all of his external funding seized by his department, so that any awards he wins do not benefit his pocketbook, though they do of course burnish his CV. By contrast, my graduate department has a more generous policy—while we (understandably) don’t keep the entirety of our awards, we see a significant financial benefit from most.
Beyond the benefit to your financial security, writing clearly and powerfully about your work is a critical skill. If you continue as an academic beyond grad school, much of your working life will be dominated by grant applications, in which you must persuade funding committees that your research is important and interesting, and that your lab is the best group to pursue it. Graduate student funding applications are a microcosm of this. Even if you should leave academia, however, the skills you will develop through graduate funding applications are worthwhile. Especially in computational biology, the applications will force you to consider your work from different perspectives—awards from engineering or computer science bodies require you to consider the technical novelty of your project, while awards from healthcare- or biology-focused agencies necessitate that you justify your project with respect to the applied outcomes it can achieve. Answering these questions by applying for awards at the outset of your project, and then revisiting them as you progress, can help you understand what aspects of your project will be valuable to the wider research community.
I hope this perspective is valuable to someone facing the same questions I had a bunch of years ago. I’m extremely grateful to the people who’ve guided me along this path, with my grad and undergrad PIs Quaid Morris and James Wasmuth at the top of this list.