I studied math and physics at MIT. To be more precise, I started in math as default—ever since I was two years old, I’ve thought of myself as a mathematician, and I always did well in math class, so it seemed like a natural fit.
But I was concerned. In high school I’d been in the U.S. Mathematical Olympiad training program, and there I’d met kids who were clearly much much better at math than I was. In retrospect, I don’t think I was as bad as I’d thought at the time: there were 24 kids in the program, and I was probably around #20, if that, but I think a lot of the other kids had more practice working on “math olympiad”-type problems. Maybe I was really something like the tenth-best in the group.
Tenth-best or twentieth-best, whatever it was, I reached a crisis of confidence around my sophomore or junior year in college. At MIT, I started right off taking advanced math classes, and somewhere along the way I realized I wasn’t seeing the big picture. I was able to do the homework problems and do fine on the exams, but something was missing. Ultimately I decided the problem was that, in the world of theoretical math, there were the Cauchys, the Riemanns, etc., and there were everybody else. I didn’t want to be one of the “everbody else.” Unfortunately I didn’t know about applied math at the time—at MIT, as elsewhere, I imagine, the best math students did the theory track.
I was also majoring in physics, which struck me as much more important than math, but which I felt I had even less of an understanding of. I did well in my classes–it was MIT, I didn’t have a lot of friends and I didn’t go on dates, so that gave me lots of time to do my problem sets each week–and reached the stage of applying to physics grad schools. In fact it was only at the very last second in April of my senior year that I decided to go for a Ph.D. in statistics rather than physics.
I had some good experiences in physics, most notably taking the famous Introduction to Design course at MIT—actually, that was a required course in the mechanical engineering department but many physics students took it too—and working for two summers doing solid-state physics research at Bell Labs. We were working on zone-melt recrystallization of silicon and, just as a byproduct of our research, discovered a new result (or, at least it was new to us) that solid silicon could superheat to something like 20 degrees (I think it was that, I don’t remember the details) above its melting point before actually melting. This wouldn’t normally happen, but we had a set-up in which the silicon wafer was heated in such a way that the center got hotter than the edges, and at the center there were no defects in the crystal pattern for the melting process to easily start. So it had to get really hot for it to start to melt.
Figuring this out wasn’t so easy–it’s not like we had a thermometer in the inside of our wafer. (If we did, the crystalline structure wouldn’t have been pure, and there wouldn’t have been any superheating.) We knew the positions and energies of our heat sources, and we had radiation thermometers to measure the exterior temperature from various positions, we knew the geometry of the silicon wafer (which was encased in silicon dioxide), and we could observe the width of the molten zone.
So what did we do? What did I do, actually? I set up a finite-element model on the computer and played around with its parameters until I matched the observations, then looked inside to see what our model said was the temperature at the hottest part of the wafer. Statistical inference, really, although I didn’t know it at the time. When I came to Bell Labs for my second summer, I told my boss that I’d decided to go to grad school in statistics. He was disappointed and said that this was beneath me, that statistics was a step down from physics. I think he was right (about statistics being simpler than physics), but I really wasn’t a natural physicist, and I think statistics was the right field for me.
Why did I study statistics? I’ve been trained not to try to answer Why questions but rather to focus on potential interventions. The intervention that happened to me was that I took a data analysis course from Don Rubin when I was a senior in college. MIT had very few statistics classes. I’d taken one of them and liked it, and when I went to a math professor to ask what to take next, he suggested I go over to Harvard and see what they had to offer.
I sat in on two classes: one was deadly dull and the other was Rubin’s, which was exciting from Day 1. The course just sparkled with open problems, and the quality of the ten or so students in the class was amazing. I remember spending many hours laboriously working out every homework problem using the Neyman-Pearson theory we’d been taught in my theoretical statistics course. It’s only by really taking this stuff seriously that I realized how hopeless it all is. When, two years later, I took a class on Bayesian statistics from John Carlin, I was certainly ready to move to a model-based philosophy.
Anyway, to answer the question posed at the beginning of the paragraph, Don’s course was great. I was worried that statistics was just too easy to be interesting, but Don assured me that, no, the field has many open problems and that I’d be free to work on them. As indeed I have.
Why did I start a blog? I realize I’m skipping a few steps here, considering that I started my Ph.D. studies in 1986 and didn’t start blogging until nearly two decades later. I started my casual internet reading with Slate and Salon and at some point had followed some links and been reading some blogs. In late 2004 my students, postdocs, and I decided to set up a blog and a wiki to improve communication in our group and to reach out to others. The idea was that we would pass documents around on the wiki and post our thoughts on each others’ ideas on the blog.
I figured we’d never run out of material because, if we ever needed to, I could always post links and abstracts of my old papers. (I expect I’m far from unique among researchers in having a fondness for many of my long-forgotten publications.)
What happened? For one thing, after a couple months, the blog and wiki got hacked (apparently by some foreign student with no connection to statistics who had some time on his hands). Our system manager told us the wiki wasn’t safe so we abandoned it and switched account names for the blog. Meanwhile, I’d been doing most of the blog posting. For awhile, I’d assign my students and postdocs to post while I was on vacation, but then I heard they were spending hours and hours on each entry so I decided to make it optional, which means that most of my cobloggers rarely post on the blog. Which is too bad but I guess is understandable.
Probably the #1 thing I get from posting on the blog is an opportunity to set down my ideas in a semi-permanent form. Ideas in my head aren’t as good as the same ideas on paper (or on the screen). To put it another way, the process of writing forces me to make hard choices and clarify my thoughts. The weakness of my blogging is that it’s all in words, not in symbols, so quite possibly the time I spend blogging distracts me from thinking more deeply on mathematical and computational issues. On the other hand, sometimes blogging has motivated me to do some data analyses which have motivated me to do new statistical research.
There’s a lot more that I could say about my blogging experiences, but really it all fits in a continuum with the writing of books and articles, meetings with colleagues, and all stages of teaching (from preparation of materials to meetings with students). One thing that blogging has in common with book-writing and article-writing is that I don’t really know who my audience is. I can tell you, though, that the different blogs have much different sets of readers. My main blog has an excellent group of commenters who often point out things of which I’d been unaware. At the other blogs where I post, the commenters often don’t always understand where I’m coming from, and all I can really do is get my ideas out there and let people use them how they may. In that way it’s similar to the frustrating experience of writing for journals and realizing that sometimes I just can’t get my message across. In my own blog I can go back and continue modifying my ideas in the light of audience feedback. My model is George Orwell, who wrote on the same (but not identical) topics over and over again, trying to get things just right. (I know that citing Orwell is a notorious sign of grandiosity in an author, but in my defense all I’m saying is that Orwell is my model, not that I have a hope of reaching that level.)