Tips for Succeeding in a CS Ph.D.

March 29, 2020

Now that I am wrapping up my Ph.D., an endeavor that has defined my last four years, it seems as good a time as any to take stock of the advice I’ve given and received. I’m recording this mostly for myself, in hopes that a few years from now I might do this again and compare notes. However, I also hope that if any up-and-coming grad students stumble on this list, maybe it might do some good. Also, given there’s a pandemic outside, this seems as good a time as any to wax philosophical.

I did my best to only include information I found useful. I’m very aware that a lot of this advice is contradictory, at least in part. Take all of this with a grain of salt. I am graduating from Clemson University with a Ph.D. in computer science, a focus on machine learning, and will be starting a career in industry. While I hope you find this information useful, results may vary. I find advice like this to be better interpreted as a set of proverbs, rather than a set of rules. Following all of it is probably impossible, but following none of it probably won’t work out.

Other People’s Good Ideas

Looking back on the last four years, I’ve internalized a lot of advice that I lean on pretty frequently. At this point, I can’t really tell whether its something I’ve come up with, something an adviser said, or something I read online. While I’ve done my best to credit others where possible, I certainly don’t want to take credit unduly. Additionally, I don’t want to do you, the reader, a disservice by poorly restating someone else’s advice. So maybe now is a good point to tab over to Matt Might’s articles, and specifically read through the Illustrated Ph.D. and Why Ph.D. Students Fail. Furthermore, if you can grab a copy, I really recommend skimming The Pragmatic Programmer (here’s a list of the tips from that book if you can’t find a copy). Having recently revisited these sources, I was amazed to see so many tidbits that I’ve repeated over and over originated here.

For instance, the Pragmatic Programmer drills in right away that good programmer as those who:

Care About Craft.

Why spend your life developing software pursuing this degree unless you care about doing it well?

This applies doubly to Ph.D. students. I found it incredibly important to strive for best practices. The more rigorous your procedures, the greater rationale you will develop to support your choices. You need a rational for your choices. At first, your adviser will ask about them, and eventually your reviewers and conference attendees will challenge them a lot less nicely. Developing a craft, and understanding the tools you use, the algorithms you choose, and the software systems you rely on will all have consequences to your conclusions. Be aware of them.

Now, all of the above references above discuss the mechanics of research and programming. However, you will quickly find, as a new Ph.D. student, that your endeavor will require a whole set of soft skills—writing, speaking, teaching, and presenting. At a high level, this means you need to:

Put a high weight on soft skills.

If a paper gets published in a journal, and no one is there to read it, does it make an impact?

Like falling trees, publications are only important if observed. There are a range of ways to increase the probability that someone observes your work. Firstly, you need to do good research. But, once you have that fated plot that demonstrates the potential benefit of your wonderful new idea, you still have a lot of work to do. The first soft skill in this process is writing. I was given Strunk & White and picked up On Writing Well really early in my grad-student career. The former is a list of practical writing tips stated clearly enough for me (a CS student) to figure out. The second is a little softer, and expands on the intuitions behind those tips. Sure, they are written from the perspective of writing nonfiction broadly, but those principles are foundational to scientific writing.

This is particularly important because:

You're here to write papers, not software.

Researchers are evaluated by $h$-index, not lines of code. Sure, you need to write code to get a high $h$-index, but good papers are published with terrible software all the time.

There is a lot of truth regarding the above tip, which I received from my undergraduate adviser before starting my Ph.D., but I would recommend not following this one all the way. Surely, I have seen some terrible research code, but for any project lasting more than a couple of months, or exploring more than one research topics, you’re going to be revisiting and revising often. As a result, you should find a balance around the mantra “good enough is good enough.” I have a lot more to stay on the practice of writing research software (its hard), but that’s probably going to have to wait for another post.

Unlike these references on writing, I’ve never come across good works on presentations. However, a professor I had in undergrad gave me some really good advice:

No more than 5 points per slide, no more than 5 words per point, use pictures whenever possible.

Your audience is not there to read. You wrote a whole paper that already explains those details.

This point is a little controversial, but I’m firmly in the camp that simpler presentations are more effective than complex ones. The other side of this argument believes that if someone comes across your slides published online, they should still be able to read all of the key points of your talk, without you actually there to speak. This is flawed logic. The vehicle to present your work in written form is the research paper, so why shoehorn a presentation to act as a redundant version of that? Instead, your presentation should provide only the scaffolding necessary to give your talk—the bent wire on which you will add clay from behind a podium.

The reason you want simpler slides is because you do not want to confuse your viewers. A paragraph of text screams “read me!” So, many in your audience will start to read, which necessarily means they will cease to listen. In contrast, without any text, your audience will not know what aspects of your dialog are the most important, what words are going to have implications later in the talk, or what take-aways they should dwell on. The correct balance is achieved through short sentence fragments. Distill a complex thought, such as “Algebraic distance is an iterative relaxation process that places each node in the input graph on the unit interval,” to “Algebraic Distance: Iterative relaxation process. Assigns nodes coordinates in $[0,1]$.” New slide.

Lessons Learned

As previously stated, I don’t want to take credit for any of my own advice. If you find this useful, I’m probably forgetting where I heard these tips from. However, a sign that I made these up myself would be if you think they’re totally off base.

To start, I want to focus on the research process itself:

Limit Complexity

If $X$ is a problem, are we going to publish a whole paper on $X$, or do we just need a solution to $X$ in order to publish a paper on $Y$?

Research papers are fundamentally argumentative writing. You spend months coming up with ideas and running experiments to collect evidence in support of your hypothesis that your reviewers will be (correctly) biased against. Therefore, you must convince a reader by building from a set of accepted propositions and experimentally backed claims. The more your argument relies on these accepted prepositions, the less burden you place on experiments. Every time you come up with a special solution, you will also be expected to defend your choice. As a result, your whole research project should only contain ONE special solution.

Here’s a concrete example. Imagine we have a new idea for a centrality measure for nearest-neighbors graphs, and we want to try it out on a particularly large and high-dimensional dataset. The new grad student may run into a number of problems simply creating a nearest-neighbors graph from this large dataset. This is an example of a problem that you need to solve before doing any research. At this point, the grad student might think of a special case of hashing function to help construct this nearest-neighbors graph. This very likely is a bad idea. The reasons for this can be briefly summarized as:

Corollary: Don't Reinvent the Wheel.

It sounds so simple, but it turns out there’s a lot of wheels out there.

If the grad student can provide a citation for the locality-sensitive hashing function they have used, then this technique becomes a proposition to their ultimate argument. If they cannot, a good reviewer will ask “why that function and not another?” At that point, the grad student might have to run multiple experiments using a range of potential hashing functions. In effect, the sub-problem of constructing a large nearest-neighbors network has become a central point to the work. No one wanted this, especially because there are many existing ways to construct large nearest-neighbors networks (and there are probably many ways to solve your sub problem too). So take some time searching Google Scholar and GitHub before embarking on a sub problem.

If the reason we want to limit complexity is because we want to create arguments that are easier to defend, then this means that our whole research process should be built around defending our day-to-day choice. It is for this reason that I think its so important to:

Take notes
If you ask 5 Ph.D. students about note taking, you're going to hear 6 strategies. However, you should have some strategy.

Notes are incredibly important to help your day-to-day decision making. You should be able to respond to the question “why did you do that?” or “what else did you try?” by opening your notes, turning to that date, and reciting whats there. This means you’re going to be taking notes with the knowledge that you probably will only reference 1% of the notes you ever write, but that 1% is so valuable that you’re going to have to build a habit.

Everyone learns differently, and it takes a long time to create a craft around note taking. My exploration converged to bullet journals written in cheap paper notebooks with a nice pen. I start each day by hand writing the date, and reviewing the todo list of that has been recorded over the last few days. I try to add a line for every task I start, stop, change, or think of. This way, when asked a question, I can find the date I worked on whatever it is, see the work I did that day, and any results that may have influenced my thinking.

Working with Others

At some point, you’re going to be in a room with someone who wants you to work on their project. Your adviser might have put you there, or a friend is going to pitch something over lunch. Regardless, I learned the hard way that collaborating in computer science has some pitfalls. I wish that I had started my Ph.D. thinking:

If you're going to help, be a helper.
If you have a choice, don't agree to work you don't want to follow through with. If you don't have a choice, the work is not going anywhere. In either case, if you agree to help someone, help them wholeheartedly.

I got this idea partially from Lean In wherein Sheryl Sandberg describes hiring someone because instead of thinking about how the job could benefit the candidate’s career, the candidate instead wanted to learn what problems Sandberg was facing in order to see if they could help.

This is how you should approach collaborations, if you are going to collaborate. Hopefully, the reason this collaboration makes sense is because you have already obtained some expertise needed by a colleague. If things go well, with relatively little extra work on your part, this could turn into a paper with your name on it. But, this also means that you need to be flexible, and focus more on the problems and timeline of your peers, rather than your own personal research objectives.

It is exceptionally rare for collaborations to align with your own personal research direction, unless you or your adviser are the one’s searching for teammates. This means that you must:

Beware trivial collaborations.
You should ultimately feel like you are gaining something from a collaborative work, even if it is only your name on a paper outside of your field. Do not agree to be tech support.

For every fruitful collaboration, there are a number of dangerous time sinks. Most collaborations are not along your critical path to a Ph.D. These extra-circulars can be very interesting and provide a new perspective on your work, and in this case you should be the best helper you can be. If instead you are asked to “write some code” or “run some experiments” you should be weary. Your selfish goal is to learn and gain expertise. Furthermore, your altruistic goal is to apply your expertise to help other’s gain their own. No one is helped if you become the “linux expert” in a group. This is not research.


Having covered some specifics of the Ph.D. process, I think its most important to remember:

It's not a sprint, it's a marathon.
A journey of a thousand miles behind with just one commit.

The average Ph.D. in CS is between 4 and 6 years long. That means you probably should take weekends whenever possible. That means you probably need a hobby. That means that not every day is going to be your best work. Sure, as Matt Might pointed out, “Ph.D. school is a monastic experience. And, a jealous hobby." And sure, you should expect to work some late nights and weekends. However, many otherwise successful students burn out brightly and quick.

The way you get around burnout is to keep your goals in mind, and to find purpose in the work you’ve chosen to do. Make sure that you:

Corollary: Enjoy your research topic.

You’re going to be thinking about this while you: wake up, go to sleep, eat lunch, shower, talk to your significant other, and at midnight in your office the day the paper’s due. You should probably like it.

This is easier said than done. Personally, I just got lucky and stumbled on the most interesting project I’ve had the pleasure of working on to date. However, if you’re entering a Ph.D., you should probably be the sort of weirdo who finds the idea of pondering a project for a few years exciting.

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