Statistical genetics for all occasions
After about 20 years in academic science (25 if you count volunteering in a lab as an undergrad), I finally decided to go my own way. My academic career path has been unorthodox up to now, and I have been lucky to have the support of great advisers along the way. However, the mismatch between what I want to do and what is valued for the purposes of academic career advancement has become impossible to ignore. So instead of struggling to reconcile the incompatible I decided to try a different approach.
In order to advance in academic science one generally has to publish as many first (or senior) author papers as possible. While quality is not completely irrelevant, it does seem to come second to quantity. To support his or her projects, a researcher also must secure funding, mostly from the government. The latter seems to go more and more to groups of scientists doing Big Science, and the size that matters is measured in terabytes of data rather than impact on our understanding of nature. This point seems to be in conflict with the first-author publication requirement, since in these large consortia a number of scientists make approximately equal contributions. How (or if) this gets resolved is not obvious to me.
The way I enjoy doing science, though, does not seem to sit well in the current system. For one, I like to work on hard problems that I am not fully qualified to tackle. This means learning as I go, which takes commitment for a much longer stretch of time than is acceptable for the current publication speed requirements. As a result of this approach I have acquired expertise in a wide variety of fields, both experimental and computational. Statistical methods I learned turned out to be both unusual and useful enough that I ended up lending a hand in a number of projects. Since I was just the computational and data analysis support person, however, my contributions did not rise to the level of first authorship (appropriately so). In addition, realizing that my approaches can have a real-life use in practical breeding applications, I became interested in helping practical breeders achieve their goals by providing data analysis and training. These types of activities are not frowned-upon in academia, but they do not add significantly to one’s portfolio of achievements required for career advancement.
At the time I was realizing that my interests started to diverge from the standard academic path, I was working on increasingly computationally demanding projects. As part of that, I started using Amazon Web Services. This made me realize that I hardly need any infrastructure to do this work on my own. For even the most computationally intensive work, all I need is a reasonably good laptop and an internet connection. I was also impressed by a number of independent tech researchers. For example, Steve Gibson sells a piece of software to support himself but releases the bulk of his work for free. It became harder and harder to justify continuing to try and fit into the academic model.
This summer I finally decided to make the jump. I created Bayesic Research LLC to pursue my interests in applying Bayesian hierarchical models to quantitative genetics, particularly in plants where large-scale multi-site experiments with complicated replication designs are commonplace. I am working on method development, software implementation and pursuit of interesting fundamental questions in genetics. I am applying what I learn from these activities to practical breeding programs, focusing on the needs of the developing world, currently in collaboration with IRRI and CIMMYT. I do not plan to charge anyone for this work, except perhaps to cover costs in travel and computational time, neither do I believe in charging for copies of software I generate. The source of the major C++ library I am developing is available on GitHub under the GNU public license, and the same will be true of any other software projects. To support this endeavor, I am looking for commercial clients who need consulting or custom data analysis. I estimate that if I spend about a third of my time on doing paid work, I can generate enough revenue to maintain my research activities.
I think that my particular set of skills and interests makes this kind of experiment practical, but it is unclear how generalizable this course of action is. I do believe that it has promise as an alternative career path, and would very much like to hear if anyone else has had or is planning a similar move. I will periodically post about my experiences here.