In August 2022, after almost two years as a tenure-track Assistant Professor of Computer Science, I resigned from my position at the University of Vermont. This was not my preferred path, but something I felt I had to do in light of some family stressors and an increasingly untenable mismatch in values between me and my institution. Thus I joined my Millennial brethren in the Great Resignation.
Last week, Kaleigh Clary presented her work, Stick it to the Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks at USENIX Security. One of the things I really like about this work is the focus on analysis and implications of non-cooporative actors in network experiments from a PL/systems perspective.
Here in the MaPLE lab, we treat the techniques students cultivate in their programming systems classes as general methods for answering a broad range of research questions that arise in and around software when it is used in nontraditional ways. As a result, students without formal training in the techniques we use often ask: "What is PL?," whereas those with training often ask: "How is what you do PL?"
The PhD is a professional degree and like all professional degrees, it prepares students for what are fundamentally jobs in communication. While the core of the work is technical — e.g., programming/coding, writing proofs, performing empirical analyses — that work is all for naught if you cannot communicate the fundamental insights and significance of your work to others.
This is why one of the things I always tells new graduate students is to write every day.
This post is going to be more personal essay than documenting views that can masquerade as advice. I am considering writing more posts in this style, as a representation of a particular time and place. Inspired by this series.
Talking about technical work — and research especially — is hard. When we say this, we often elide the why and as a result perpetuate the idea that there is something inherently difficult about computer science. This post is about a view I've long held, that computing is just as much about learning a dialect and culture as it is about the technical work we do.