Yesterday, John Reynolds passed away. I had the privilege of being one of his graduate students, and much of what I know about what it means to be a scientist is due to his example. I just want to share a few small anecdotes about him.
When I was a new graduate student, I began working on the project which would eventually become my thesis. When I described one of my early approaches to this problem, he asked a series of questions about how I would handle one sticky technical point after another, and I showed him how the language and its semantics were carefully set up so that this issue in question could not arise. After I finished my explanation, he nodded thoughtfully, and told me, "Neel, you've done some very good engineering, but engineering is not science! Good engineers develop a sense of how to avoid obstacles -- but a scientist's job is to face them head-on and flatten them."
This made a really big impression on me. As academic computer scientists, we have two great luxuries: we can choose what to study, and we can spend as long as necessary studying it. As a result, our ethical obligation is one step removed from solving problems: our role is to produce understanding, so that engineers (indeed, all of society) have new methods to solve new classes of problems.
Once, John told me about some of his work at Argonne National Laboratory, where he designed the COGENT programming language, which he told me was what convinced him that he should leave physics to do computer science. He said that while today he could (indeed, many people could) design much better languages, he would always regard it fondly, since it was his first successful language design.
I asked him what made it successful, and he told me that there was another scientist at Argonne, who used it to write some simulation code. When John read the program listing, he realized that he could not understand how the program worked, since it relied domain knowledge that he lacked. So this was John's definition of a successful language design: if you have a user who has used it to write a program you couldn't have, your language has succeeded, since it has helped a fellow human being solve one of their own particular problems.
I've always liked his definition, since it manages to avoid an obsession with nose-counting popularity metrics, while still remembering the essentially social purpose of language design.
Another time, I asked John what he thought about object-oriented programming. He told me he thought it was too soon to tell. I burst out laughing, since I thought he was joking: Simula 67 is considerably older than I am, and I thought that surely that was sufficient time to form an opinion.
But he insisted that, no, he was not kidding! From a formal, mathematical, perspective, he did not like object-oriented programming very much: he had studied various formalizations of objects in his work on Idealized Algol and on intersection types, and found they introduced some unwanted complexities. However, so many excellent engineers still thought that objects were a good idea, that he was unwilling to dismiss the possibility that there was a beautiful mathematical theory that we haven't discovered yet.
I thought this was a great demonstration of Keats's negative capability, but what makes it really impressive was that John married it to a profound intellectual rigour: I've never known anyone who could produce counterexamples faster than John could. While he tested every idea ruthlessly, he never closed himself off from the world, giving him a rare ability to make judgements without passing judgement.
I haven't really said anything about John's enormous scientific achievements, because the deepest lesson I learned from him is not any one of his theorems, but rather his fundamentally humane vision of science, as something we do for and with our fellow scientists and community.
Ars longa, vita brevis, but together we make a chain through the ages --- and more links of that chain than most can claim were forged by you, John.