- Be ready to start over or shift gears at any time. Research is not easy. Often times, things don’t quite go as planned. You might have had some idea in your head how something would turn out, but after working on it awhile, you just couldn’t solve it or it turned out not to work. Or your research might end up going in a totally different direction than you initially “planned.” That’s totally fine! There have been many times when a project that I initially conceived turned out totally differently — either because I found out the ultimate conclusion wasn’t what I was expecting, or because I had discovered that another researcher had already done something very similar (then I was kind of forced to change the scope of my project if I wanted to be able to publish my work). The unpredictability is partly what makes research so exciting for me.
- Rejection gets easier over time. The first paper I wrote as a PhD student was actually rejected four times before finally being accepted. I have also gotten several other rejections on manuscripts over the years. At first, it was quite crushing to have worked so hard for months only to have my work be rejected. But over time, I have learned to deal with it.
If you are aiming for more prestigious journals or conferences where the acceptance rate hovers around 10-20 percent, then it will naturally be more difficult to get your work accepted. I do recommend “aiming high,” so I do often initially try the most prestigious venue I can think of that could be a good fit for my work. I’ve occasionally had success and gotten an invited revision from one of these venues, but other times, I haven’t been able to. So I just revised my work according to the suggestions from the reviewers and tried another decent journal.
If you have a particularly good idea, then keep “milking” the idea for several papers. The first paper from my postdoc was finally accepted in a top field journal this past year. Since I was intimately familiar with the topic, I followed it up with two other papers (here and here) that were related but that still made novel contributions. The first paper was about a group regularization prior used in the contexts of Gaussian grouped linear regression and Gaussian additive models. In my follow-up works, I extended the use of this prior to varying coefficient models and to non-Gaussian data via the generalized linear framework. The contributions in my follow-up papers were still original in their own right, but they built upon tools that I was already familiar with.
If you are hoping for an academic career at a research university, then quantity does matter. Of course, quality matters the most (so articles in “top” journals and conferences count for more). But you definitely need more than just one “major” paper in order to guard against potential claims of poor productivity for promotion (e.g. from Assistant to Associate Professor). Because the entire publication process could take well over a year from first submission to final acceptance in my field, time is of the essence. It is typically easier to conceive new papers that extend your existing work in new directions, so I recommend doing that as much as possible.
- At the same time, keep learning other topics so you can move onto something new in the future. While I have a lot of other ideas how to possibly extend my current work, I am hoping to have some of my future PhD advisees work on them instead of me “hogging” all the ideas. Plus, even the best ideas could eventually become so commonplace (or even obsolete) that it becomes much harder to publish papers on them in good journals. Given this fact, I know that I always have to be ready to “move on,” so to speak. In view of this, I try to spend a good chunk of my research time just reading about and fostering collaborations with other researchers on new topics, especially in emerging fields of interest where there isn’t as much work done.
To this end, I have been currently exploring new approaches to uncertainty quantification in complex Bayesian models, especially with non-Gaussian data (e.g. sparse and/or correlated binary data). There is a lot of exciting work to be done with regard to both scalable Bayesian inference algorithms and the theoretical underpinnings.
You’re never truly “prepared,” so try not to worry too much about it and just do your best. I have basically been “underprepared” for everything at every stage of my professional life. Whenever I have changed careers (before returning to academia, I worked in engineering and finance), I have had to learn an entirely new field and familiarize myself with a completely new set of tools. Now, whenever I decide to pursue a new research direction, I usually only have a vague idea about the topic. To become more familiar with the topic, I need to spend quite a bit of time teaching myself the subject, reading the relevant literature, and learning a new set of relevant tools.
As I begin the next stage of my career, there is still so much that I don’t know. As an Assistant Professor at an R1, I will need to learn about obtaining external grant money, supervising PhD students, and teaching graduate students. These are not things that I have any prior experience with. It can all be very intimidating. But I just remind myself that I have been in this position of “unpreparedness” before, and that I can learn to excel at it if I work hard at it, ask questions, and gain a little bit more experience.
I am moving to Columbia, SC at the end of this week to begin my new career as an Assistant Professor at the University of South Carolina. I have been doing academic research for four years now. I began doing research after passing my PhD qualifying exams in the summer of 2016. Following completion of my PhD in 2018, I completed a two-year research postdoc. After four years, I can say that I have learned quite a bit. Before beginning the next phase of my academic career, I figured it would be a good idea to write down all that I have learned in the past four years.