One of the fortunate aspects of being a Professor in Academia is that you get to meet the future on a regular basis: Part of your job is to interview Ph.D. candidates and/or present to undergraduates contemplating research programs (Penn’s SUIP is a terrific example, but of course, there are many others). For me, this means presenting to undergrads all across the country as part of the Diversity and Outreach Committee I chair, and interviewing candidates for multiple PhD. programs that come from diverse backgrounds: Computer Science (Engineering School), Genomics and CompBio, Genetics & Epigenetics, and last but not least MSTP (Penn’s MD/PhD program).
One optimistic observation I could make is that our future looks very bright: The students are top-notch. Their background knowledge and experience is so much more advanced than what it used to be even 5-10 years ago (not to mention our era as students…;), they are motivated, smart, and ask outstanding questions. This leads me to today’s post: Should I do a PhD?
I think this is a question that many undergraduate students don’t spend enough time contemplating, or are sometimes unaware of some of the considerations. I will focus here on CompBio/Applied ML/Genomics, which are the areas of my work, but many of the points discussed below are more general.
A common approach to this question is to think about it as a business transaction: How much time will it take, what would be the financial price, what will be the gain in terms of increased salary/opportunities, etc. This is definitely a valid approach and important consideration: You don’t want to run into a “glass ceiling” of opportunities and salaries blocked due to lack of a higher degree, and from what I hear, this is definitely the case for various industries involving both Genomics/CompBio and ML. The reasonable assumption is that a PhD may cost a few years of low income but can enable you to attain research-intensive and leading roles that are mostly open to those with a PhD. That said, trying just to maximize potential future revenues could also backfire: If you are not particularly good at research and end up with a formal PhD that didn’t get you far, you may find yourself “stuck” with a PhD: You are considered overqualified to jobs you would actually love to get, but not a good fit (or not interested) in positions available to a PhD. Keep in mind that even if that is the case, you could still use the skills you learned during your PhD and transition to a non-research-centered position that would make you happy: Teaching at a high school/college, working for the government, publishing, not-for-profit organizations, etc. A PhD done right should help you develop many skills that could help in other domains – coding (for comp people), communication, teaching, writing, managing projects, prioritizing tasks, etc.
While all of the above are valid considerations, these are not the ones I want to focus on here. Instead, I want to focus on the “Is a PhD really for me?” aspect. My impression is that many undergraduates have unrealistic expectations of what a PhD is about. So let me highlight a few issues and dispel some common misconceptions:
- “I’m going to cure cancer with machine learning”: This is something I see more from students capable in CS/math but with little hands-on experience in genomics and genetic research. They get excited about “ML for good” and curing disease with their newly acquired skills. I very much sympathize with this motivation, but being inexperienced in such research, they think they are going to wow a problem into being solved by a cool DL algorithm. They lack realization of what research in Applied ML/CompBio entails, which is what I discuss next.
- “Comp bio is 95% ‘dirty work’: Cleaning data, getting your code to work, getting your model to work, analyzing the results, and validations that don’t pan out. Not so much thinking up glorious models/algorithms. (and no, I don’t have quantitative assessment to base the 95% number on, but there is a lot of that, same for the next item below)
- Research is 95% failure, and it’s a long game: Many young students are used to continuous success – in their courses, exams, and in sport tournaments. These also require only bursts of effort. But a PhD is a long game that requires stamina and grit. And you don’t want just to survive it – you want to shine, do great work. Yet, paradoxically, you have to accept/realize that most of the time you fail. Which leads us to our next point.
- Papers are completely misleading. You only read about the eventual successes and they are always laid out in this wonderful logical progression. But that is an unrealistic and misleading view of how Science actually works. You try A to prove B, A doesn’t work and you end up doing C (or, more likely, C1/C2/C3 etc) which shows D. Then you end up with a paper describing how we set out to solve D with this elegant C3 model or experiment. Uri Alon has has an inspiring talk about getting lost in the research cloud and finding your way. And while the talk is insightful and inspiring (highly recommend), it is built to help scientists and scientists, not answer our question (“Should I do a PhD?”). Which brings me to the next point.
- Research is dealing with uncertainty and unknowns. All the time. You may not like it. Many tend to think exploring the unknown is exciting, something they would want to do if just given the option. But in reality, I suspect majority of people would be much happier with certainty: Certainty in our society and world view, our income, and our work. We want to know that if we do a good job on solving well defined X as set by our boss(es), then we will progress, get a bonus, feel pride in our work etc. We don’t want to spend years on a research question which turns out to be unsolvable or the results suck. We don’t want to try and figure out all the questions to build a research program. We would much rather have someone else do that. And that’s 100% totally OK: open ended volatile research questions are not everyone’s cup of tea. You just need to recognize if that’s the case for you; maybe go become for example an incredibly capable/creative developer instead – more happy, fulfilled and accomplished.
I realize the above list may be demoralizing for some. To be clear, I am not advocating against doing a PhD. I love what I do and I think that if you ask around my lab you will find people love (while suffering the above…;) doing research. But I do think it’s not necessarily for everyone. Maybe the best advice I can give to young undergraduates is to get some hands-on experience, preferably more than one, to see if this is for them (see point above about SUIP and similar programs). More generally, as someone who hasn’t grown up here, I find that North American culture cultivates rushing for external achievements as fast/as soon as possible. Young people rush to college before they figure out what they want to do/study (while at the same time they are not used to failing, see point above). Then they rush to a PhD. In many cases, they are better off growing up a little first – figuring out what they like/want to do, get some valuable experience in research or in industry. Such experience can help wonders in your coding skills, prioritization, time management etc., which will help you get more out of your PhD. It could also help you figure out who you are. But that’s maybe a topic for another blog post in the future.
I hope to follow up on some of the points I made above in the near future. I want to explore answers to great questions I got asked by students this recruitment season, so stay tuned!
Before I end, let me just mention a few other past posts by me and others that may be useful for students contemplating their next step in research or towards a PhD – I hope you find these useful!
Navigating Grad School Admissions in Science | by Talia Lerner
Why do you want to be a scientist?
Thoughts on picking a rotation lab – Avasthi Lab
What does it take to get a PhD?
The Art in Science – Part III: “What problem should I choose to work on?”
Advice for Picking a Graduate Advisor | ZarLab
Proper upbringing vs negligence of computational students in biomedical research environments
(this last one is not about PhDs but about Bioinformaticians in wet labs. It was recommended to me by a student I interviewed this year – I thought it was a great commendation and promised to share next time I write about related topics…😀)
The BioCiphers Lab Blog 