I was lucky to have research grant support and minimal TAing duties this fall, so all semester I’ve felt my research was chugging along productively. Yet I have less to show for it than last semester—I went a little too far down an unrewarding rabbit-hole. Knowing when to cut your losses is an important skill to learn!
Having defended my proposal this summer, I spent a lot of time this fall attacking one main sub-problem. Though I always felt I was making reasonable progress, eventually I discovered it to be a dead-end with no practical solution. I had wondered why nobody’s solved this problem yet; it turns out that it’s just inherently difficult, even for the simplest linear-regression case! Basically I wanted to provide finite-sample advice for a method where (1) the commonly-used approach is far from optimal but (2) the asymptotically-optimal approach is useless in finite samples. I think we can salvage parts of my work and still publish something useful, but it’ll be much less satisfying than I had hoped.
Working on a different problem, it felt encouraging to find errors in another statistician’s relevant proof: I felt like a legitimate statistician who can help colleagues notice problems and suggest improvements. On the other hand, it was also disappointing, because I had hoped to apply the proof idea directly to my own problem, and now I cannot 🙂
On a third front, my advisor invited another graduate student, Daren Wang, to help us wrap up a research project I had started in 2015 and then abandoned. Daren is bright, fast, and friendly, a pleasure to collaborate with (except when I’m despairing that it only took him a week to whiz through and improve on the stuff that took me half a year). Quite quickly, we agreed there’s no more to be done to make this project a much-better paper—so let’s just package it up now and submit to a conference. It was satisfying to work on writing and submitting a paper, one of the main skills for which I came to grad school!
Finally, I was hoping to clear up some stumbling blocks in an end-of-semester meeting with several committee members. Instead, our meeting raised many fascinating new questions & possible future directions… without wrapping up any loose ends. Alas, such is research 🙂
As I’ve noted before, I audited Jordan Rodu’s Deep Learning course. I really liked the journal-club format: Read a paper or two for every class session. Write a short response before class, so the instructor can read them first. Come prepared to discuss and bring up questions of your own. I wish more of our courses were like this—compared to lecture, it seems better for the students and less laborious for the instructor.
Although it was a theory course, not hands-on, I did become intrigued enough by one of the papers to try out the ideas myself. Together with classmate Nicolas Kim, we’re playing around with Keras on a GPU to understand some counterintuitive ideas a little better. Hopefully we’ll have something to report in a couple of weeks.
I also started to audit Kevin Kelly’s undergrad and grad-level courses on Epistemology (theory of knowing). Both were so fascinating that I had to drop them, else I would have done all the course readings at the expense of my own research 🙂 but I hope to take another stab someday. One possibly-helpful perspective I got, from my brief exposure to Epistemology, was a new-to-me (caricatured) difference between Bayesian and classical statistics.
- Apparently most philosophy-of-science epistemologists are Bayesian. They posit that a scientist’s work goes like this: You are given a hypothesis, some data, and some prior knowledge or belief about the problem. How should we use the data to update our knowledge/belief about that hypothesis? In that case, obviously, Bayesian updating is a sensible way to go.
- But I disagree with the premise. Often, a scientist’s work is more like this: You’re not handed a hypothesis or a dataset, but must choose them yourself. You also know your colleagues will bicker over claims of prior knowledge. If you come up with an interesting question, what data should you collect so that you’ll most likely find a strong answer? That is, an answer that most colleagues will find convincing regardless of prior belief, and that will keep you from fooling yourself? This is the classical / frequentist setting, which treats design (of a powerful, convincing experiment / survey / study) as the heart of statistics. In other words, you’re not merely evaluating “found” data—your task is to choose a design in hopes of making a convincing argument.
Some of my cohort-mates and I finally organized a Dissertation Writing Group, a formal setting to talk shop technically with other students whose advisors don’t already hold research-group meetings. I instigated this selfishly, wanting to have other people I can pester with theory questions or simply vent with. But my fellow students agreed it’s been useful to them too. We’re also grateful to our student government for funding coffee and snacks for these meetings.
I did not take on other new side projects this fall, but I’ve stayed in touch with former colleagues from the Census Bureau still working on assessing & visualizing uncertainty in estimate rankings. We have a couple of older reports about these ideas. We still hope to publish a revised version, and we’re working on a website to present some of the ideas interactively. Eventually, the hope is to incorporate some of this into the Census website, to help statistical-novice data users understand that estimates and rankings come with statistical uncertainty.
Finally, I heard about (but have not attended) CMU’s Web Dev Weekend. I really like the format: a grab-bag of 1- or 2-hour courses, suitable for novices, that get you up and running with a concrete project and a practical skill you can take away. Can we do something similar for statistics?
Topic ideas where a novice could learn something both interesting and
useful in a 1.5h talk:
- How not to fool yourself in A/B testing (basic experimental design and power analysis)
- Befriend your dataset (basic graphical and numerical EDA, univariate and bivariate summaries, checking for errors and outliers)
- Plus or minus a bit (estimating margins of error—canned methods for a few simple problems, intro to bootstrap for others)
- Black box white belt (intro to some common data mining methods you might use as baselines in Kaggle-like prediction problems)
Many of these could be done with tools that are familiar (Excel) or novice-friendly (Tableau), instead of teaching novices to code in R at the same time as they learn statistical concepts. This would be a fun project for a spring weekend, in my copious spare time (hah!)
Offline, we are starting to make some parent friends through daycare and playgrounds. I’m getting a new perspective on why parents tend to hang out with other parents: it’s nice to be around another person who really understands the rhythm of conversation when your brain is at best a quarter-present (half-occupied by watching kid, quarter-dysfunctional from lack of sleep). On the other hand, it’s sad to see some of these new friends moving away already, leaving the travails of academia behind for industry (with its own new & different travails but a higher salary).
So… I made the mistake of looking up average salaries myself. In statistics departments, average starting salaries for teaching faculty are well below starting salaries for research faculty. In turn, research faculty’s final salary (after decades of tenure) is barely up to the starting salaries I found for industry Data Scientists. Careers are certainly not all about the money, but the discrepancies were eye-opening, and they are good to know about in terms of financial planning going forward. (Of course, those are just averages, with all kinds of flaws. Particularly notable is the lack of cost-of-living adjustment, if a typical Data Scientist is hired in expensive San Francisco while typical teaching faculty are not.)
But let’s end on a high note. Responding to a question about which R / data science blogs to follow, Hadley Wickham cited this blog! If a Hadley citation can’t go on a statistician’s CV, I don’t know what can 🙂