Reflecting on being a graduate student (in AI) in 2020
Being a graduate student is uniquely hard, but also in ways that are hard to describe. I am specifically thinking about PhD students here, but it applies to everyone to varying degrees. Why is it that I hear things from recent graduates like “there is light at the end of the tunnel” or the simple “you can do it” whenever discussing my graduation date coming up.
The broken power dynamics of a PhD program
There is something uniquely challenging about getting through a PhD. The fun fact with most of the people who say this is that the work they are doing often doesn’t change much when they leave, it is just a change in compensation, incentives, environment, and mindset.
- Compensation: being paid the actual market rate for your work removes a lot of anxiety
- Incentives: even though in industry research labs the goal may be to publish papers, there are normally far fewer webs saying things like “your papers need to form a coherent story” or “you need 3 journal papers before you can get promoted” — research is a part of a story and the incentives are better matched to that. The number one incentive of a PhD is convice my advisor to let me leave.
- Environment: graduate students almost always have shitty desk space, not great home space, not great separation of work and life. These things add up in so many ways. It is like accumulated advantage of going to good schools, having a good brand, etc., but it slowly pulls you down and weighs on people: accumulated baggage (colloquially known as jadedness).
- Mindset: this is pretty much the intangible part of being a graduate student. I think the reality of many graduate schools is better than people give it credit for. The collective mindset of graduate students and their expectations can pull each-other down to their expectations.
I have spent so much time trying to break narratives (both in my head and my friends) of “I need to be working, my advisor is in control, my advisor knows what is best, I am inadequate, etc.” It is so sickening, but real, and we need to consider how it propagates to help eliminate it. Generally, PhD program renovations (think improvements in overall wellbeing) fall short due to the dramatic power imbalance between advisor and advisee, and how the department has no cards to adjust it. Think of if a student filed an anonymous complaint about their advisor because the number of students is so low and doing things like getting a new job (lab) is near-impossible, the advisor would know who it is and the advisee has no way out. Ultimately, the only way to reform these dynamics is to have both better informed and more caring professors (ignore the problem of tenure, aging professorships, and research-based metrics for faculty jobs).
Navigating the advisor-advisee dynamics and placing yourself in the right research group is the key to graduate school. It’s a shame that lab’s and advisors come in packages because some labs would work great for people, but the advisor does not.
The context of AI research
With everything I say below, I see the AI field being amplified in almost all the challenges. AI is so lucrative and growing at a near exponential rate, so the levels of competition are naturally rising. Though, academia already is competitive enough, so piling on top of this is not a recipe for success for the field of AI itself. The level of excitement around AI is not the norm across research. I started my career in MEMs and slowly made my space in AI. It’s a mess and having one foot out of the AI pool is refreshing.
Challenges I (and all of us) have dealt with
Here is a list of things I have had to deal with because most people won’t tell you what goes wrong in their PhD, even though everyone has them.
- Failed the preliminary exam on my first try,
- Somewhere from 2-4 paper rejections on the first submission (do not want to count),
- One paper rejected after being recommended for publication by the area chair, and we still don’t know why,
- Teaching two extra semesters because funding fell through.
These are all normal things. I am happy to be making it through the program and accept it will not be perfect. Here’s the first photo I took at Berkeley. I am now going to go through some quicker thoughts.
Building models from incomplete information, a masterclass in type A stress mongering
There are definitely many types of people who go on to get a PhD, but with how competitive top programs have become it is becoming increasingly packed with type A students who started their research career early and are trying to make a big name for themselves (having more of these students is bad for intellectual diversity in my opinion).
Papers, publications, and citations
It’s slightly amazing to me that somehow most people starting (and applying for) graduate school don’t know the real process to get a paper accepted and how random it is. Multiple studies show that the peer review process at top AI conferences (see this one) overwhelmingly random. The TL;DR of what people should know:
- Papers aren’t publications: a paper is written and published at different times. See below.
- Acceptances are random: a good paper can be rejected multiple times and weird papers can win awards. It can take a year to be officially accepted and this can have a big impact on an applicant even though the research they did does not really change.
- Citations are more random: people often choose what papers to cite by a Google query — this is not a rigorous system. People often cite so much of their work and that can bootstrap a perceived as famous paper.
- Citations are field specific: different venues have different rules on how many citations are allowed per paper and what constitutes a paper. Fields like computer vision are known for having tons of citations.
I still don’t really understand these mechanics, yet they influence how I value myself sometimes. That is very silly and we should not do it.
The biggest challenge for me in 2020 has been stopping tracking how many papers I have in the pipeline, if I will have enough citations to get a job I want, and more forms of this. Ultimately, comparing yourself as a graduate student to other students is a total waste of time. Each student is one member of a small research group with vastly different circumstances.
Regardless, research and citations are not a zero-sum game, we should want everyone to succeed!
Many projects fail, so many students don’t publish all of their work. This random process over a few years of graduate study creates a huge amount of variation.
Some professors literally take years off from having students. The students that join to start the new group have to re-establish how to write a paper. Some other students are added to papers as a first year for watching experiments. The constant here is learning how to do research, not production. Citations are the observation we get from a very complicated system. Making any conclusions on the worth of a student from this point of view is sloppy and propagating problematic assumptions.
People don’t talk about the student part of graduate school
A big part of why PhD programs are harder than jobs people get is because PhD students have to do just that, be a student. Classes, teaching, operating at a bureaucratic university, and all take up anywhere from 10-40 hours a week in a given semester. We are expected to fit in a full-time research career moreover.
Not only do the hours do not add up, but the expectations around it and the mental strain cannot be ignored.
Teaching, classes, and unclear expectations
Teaching is one of the most intellectually demanding activities I have done (with pure research being near behind). No one can do these for more than 4 hours a day sustainably (the people who can do more than 4 are the super famous ones). Teaching can easily take up multiple days a week, but there are no criteria in the programs other than needing to do them to graduate.
It is often said that teaching experience can help get jobs. I did a survey of people I know in industry and 0 of them said teaching is something people directly look for, but rather traits that make someone a useful instructor often correlate successfully in many jobs. Comically, while teaching experience is useful when applying to faculty experience, it can be ignored in cases of some high-profile hires.
Finally, we have to navigate teaching and classes within the scope of our advisor. Some advisors ignore that these things exist and expect the same research output, while some encourage it but then it ultimately delays students graduating for putting off their core research. Graduate students do deserve better, but being careful with these dynamics is important.
Every student has their own funding journey (expect those joining the most famous labs, their journey was starting their hard work and being lucky early). I have written about 10 grants in my PhD and even those students with funding have to do funding reviews for work they do not necessarily care about. The research vector needed for funding and the looming dark cloud of needing to look for funding every semester tick like clockwork for some students. Not knowing how you will be paid your below market rate wage a few times a year can add an unexpected chunk of stress.
2020 and graduate school
This year was a big test. I think we are making it through, but it is obvious that most departments are not set up to handle something like this. Admissions were delayed, funding fell out, new students started remote, along with a litany of other new problems. I’m proud of my colleagues for making it through the year, but I am acknowledging that many people had to leave their programs. In a way graduate school is a privilege, but it should also do more to be accessible to all.
Backcasting: why you should not totally believe my advice
Ultimately, every time you get advice from someone it is what worked for them. What works to make one person super famous may cause harm on average across a field. Searching for and acting on advice is a slow, iterative, and reflective process.
I am happy to add more advice to these lists. Some of the more benign ones seem obvious, but they aren’t commonly practiced, so they need to be said more.
Advice I have stuck with
The best advice I received when started was “a PhD is a marathon not a sprint.” Ultimately, it is okay to be down, it is okay to have slow days, and it is okay to mess up, as long as you keep trying. Everyone burns out during their PhD, and being primed by this innocuous quote has made my recovery a little easier. Some other advice is:
- Contacting graduate students is easier than professors.
- Be positive and engage with people and opportunities will come of it.
- Have work-life balance and take care of your body.
Advice that may be good
This is mostly things that I wish I did and I am trying to figure out how to phrase:
- Focus on thinking clearly and making routines that work for you, rather than searching to please other people.
- Don’t undervalue the importance of finding the right advisor.
- Leaving with a masters is not shameful: there are so few lab positions available, that the one right for you may not have had funding. That’s unlucky, but okay.
Advice that will only work out if you are lucky
All the advice below I don’t see working because ultimately a PhD is too short to really know how the numbers work out.
- Maximize the number of papers you are on.
- Optimize for citations.
- Choose an advisor because they are famous.
A PhD should teach you how to create knowledge in a new area, and everything that happens along the way is secondary to its goals (yes, that includes papers). It is better to graduate a good researcher than as a researcher with a couple of good papers you got your name attached to. If you continue in academia, the time in your PhD will be one small segment of your career and hopefully the start of some compounding growth. If you leave academia, the skills you acquire along the way are generally all that matters anyways.
I am very lucky to be working in a field that is relevant to our short-term future (AI). I did this somewhat intentionally by listening for opportunities to try it. If you are interested in AI and want to do research in it, the best bet may be to have other interests and expertises as well.
If you are starting your journey in graduate school, good luck, you can do it, and your path is valid and impressive.