My buddy, John, successfully defended his doctoral dissertation yesterday afternoon and it was a glorious event. We are two old guys who did late career PhD’s with much joy. Honestly, I think John beat me badly in the quality of his doctoral studies and research. It is not that I did badly–I am wildly happy with what I did, but John is a force of nature. Congrats to a very dear friend.
I defended my PhD dissertation in June of 2023 at age 68. Our kids flew into Lincoln, Nebraska to watch my hooding in graduation that December when I had turned 69. I thought my formal education trajectory was over at that point, but have worked consistently on GaugeCam projects and remain an adjunct professor at University of Nebraska-Lincoln since them. In addition to that, I have reached out to Tarleton State University about their Masters program in Machine Learning and Artificial Intelligence where they were amenable to my entrance there. I have decided I would like to continue contributing in these areas as long as I enjoy it, have friends with whom I can work, God is willing. In thinking about that, it dawned on me that I could go faster in the sorts of thing required for serious academic research, which is really about pushing the state of knowledge forward as measured by the production of meaningful publications, by writing research quality software rather than bullet proof, production quality software. And when I say that, I do not mean the usual trash software produced out of even the very “best” institutions for research. What I mean is software that other, non-computer scientists can use and extend easily. To that end, I just wrote a WhatsApp message to, Troy, my fellow traveler in these circles. We will see what he has to say.
Hey Troy. I am running the 800 and 8000 random images right now. I just had a thought. Why would it be a bad thing for me to quit trying to do production level robust software where the last 90% of the effort gets you 2% more functionality? My goals in all of this are to get papers published and to push the research forward in a way that benefits our work rather than production of hardened software. To that end, it kind of makes for me to aim at making the software I write profoundly more robust than typical research software, but for research ends, not production ends. Did that make any sense? That way, we are going to publish more and get further up the conditioning curve and move on to subsequent topics much more rapidly. Thoughts?
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