This was my first PhD semester without any required courses (more or less). That means I had time to focus on research, right?
It was also my first semester as a dad. Exhilarating, joyful, and exhausting 🙂 So, time was freed up by having less coursework, but it was reallocated largely towards diapering and sleep. Still, I did start on a new research project, about which I’m pretty excited.
Our department was also recognized as one of the nation’s fastest-growing statistics departments. I got to see some of the challenges with this first-hand as a TA for a huge 200-student class.
- Statistical Computing:
This was a revamped, semi-required, half-semester course, and we were the guinea pigs. I found it quite useful. The revamp was spearheaded by our department chair Chris Genovese, who wanted to pass on his software engineering knowledge/mindset to the rest of us statisticians. This course was not just “how to use R” (though we did cover some advanced topics from Hadley Wickham’s new books Advanced R and R Packages; and it got me to try writing homework assignment analyses as R package vignettes).
Rather, it was a mix of pragmatic coding practices (using version control such as Git; writing and running unit tests; etc.) and good-to-know algorithms (hashing; sorting and searching; dynamic programming; etc.). It’s the kind of stuff you’d pick up on the job as a programmer, or in class as a CS student, but not necessarily as a statistician even if you write code often.
The homework scheme was nice in that we could choose from a large set of assignments. We had to do two per week, but could do them in any order—so you could do several on a hard topic you really wanted to learn, or pick an easy one if you were having a rough week. The only problem is that I never had to practice certain topics if I wanted to avoid them. I’d like to try doing this as an instructor sometime, but I’d want to control my students’ coverage a bit more tightly.
This fall, Stat Computing becomes an actually-required, full-semester course and will be cotaught by my classmate Alex Reinhart.
- Convex Optimization:
Another great course with Ryan Tibshirani. Tons of work, with fairly long homeworks, but I also learned a huge amount of very practical stuff, both theory (how to prove a certain problem is convex? how to prove a certain optimization method works well?) and practice (which methods are likely to work on which problems?).
My favorite assignments were the ones in which we replicated analyses from recent papers. A great way to practice your coding, improve your optimization, and catch up with the literature all at once. One of these homeworks actually inspired in me a new methodological idea, which I’ve pursued as a research project.
Ryan’s teaching was great as usual. He’d start each class with a review from last time and how it connects to today. There were also daily online quizzes, posted after class and due at midnight, that asked simple comprehension questions—not difficult and not a huge chunk of your grade, but enough to encourage you to keep up with the class regularly instead of leaving your studying to the last minute.
- TAing for Intro to Stat Inference:
This was the 200-student class. I’m really glad statistics is popular enough to draw such crowds, but it’s the first time the department has had so many folks in the course, and we are still working out how to manage it. We had an army of undergrad- and Masters-level graders for the weekly homeworks, but just three of us PhD-level TAs to grade midterms and exams, which made for several loooong weekends.
I also regret that I often wasn’t at my best during my office hours this semester. I’ll blame it largely on baby-induced sleep deprivation, but I could have spent more time preparing too. I hope the students who came to my sessions still found them helpful.
- Next semester, I’ll be teaching the grad-level data visualization course! It will be heavily inspired by Alberto Cairo’s book and his MOOC. I’m still trying to find the right balance between the theory I think is important (how does the Grammar of Graphics work, and why does it underpin ggplot2, Tableau, D3, etc.? how does human visual perception work? what makes for a well-designed graphic?) vs. the tool-using practice that would certainly help many students too (teach me D3 and Shiny so I can make something impressive for portfolios and job interviews!)
I was glad to hear Scott Murray’s reflections on his recent online dataviz course co-taught with Alberto.
- Sparse PCA: I’ve been working with Jing Lei on several aspects of sparse PCA, extending some methodology that he’s developed with collaborators including his wife Kehui Chen (also a statistics professor, just down the street at UPitt). It’s a great opportunity to practice what I’ve learned in Convex Optimization and earlier courses. I admired Jing’s teaching when I took his courses last year, and I’m enjoying research work with him: I have plenty of independence, but he is also happy to provide direction and advice when needed.
We have some nice simulation results illustrating that our method can work in an ideal setting, so now it’s time to start looking at proofs of why it should work 🙂 as well as a real dataset to showcase its use. More on this soon, I hope.
Unfortunately, one research direction that I thought could become a thesis topic turned out to be a dead end as soon as we formulated the problem more precisely. Too bad, though at least it’s better to find out now than after spending months on it.
- I still need to finish writing up a few projects from last fall: my ADA report and a Small Area Estimation paper with Rebecca Steorts (now moving from CMU to Duke). I really wish I had pushed myself to finish them before the baby came—now they’ve been on the backburner for months. I hope to wrap them up this summer. Apologies to my collaborators!
- Being a sDADistician: Finally, my penchant for terrible puns becomes socially acceptable, maybe even expected—they’re “dad jokes,” after all.
Grad school seems to be a good time to start a family. (If you don’t believe me, I heard it as well from Rob Tibshirani last semester.) I have a pretty flexible schedule, so I can easily make time to see the baby and help out, working from home or going back and forth, instead of staying all day on campus or at the office until late o’clock after he’s gone to bed. Still, it helps to make a concrete schedule with my wife, about who’s watching the baby when. Before he arrived, I had imagined we could just pop him in the crib to sleep or entertain himself when we needed to work—ah, foolish optimism…
It certainly doesn’t work for us both to work from home and be half-working, half-watching him. Neither the work nor the child care is particularly good that way. But when we set a schedule, it’s great for organization & motivation—I only have a chunk of X hours now, so let me get this task DONE, not fritter the day away.
I’ve spent less time this semester attending talks and department events (special apologies to all the students whose defenses I missed!), but I’ve also forced myself to get much better about ignoring distractions like computer games and Facebook, and I spend more of my free time on things that really do make me feel better such as exercise and reading.
- Stoicism: This semester I decided to really finish the Seneca book I’d started years ago. It is part of a set of philosophy books I received as a gift from my grandparents. Long story short, once I got in the zone I was hooked, and I’ve really enjoyed Seneca’s Letters to Lucilius as well as Practical Philosophy, a Great Courses lecture series on his contemporaries.
It turns out several of my fellow students (including Lee Richardson) have been reading the Stoics lately too. The name “Stoic” comes from “Stoa,” i.e. porch, after the place where they used to gather… so clearly we need to meet for beers at The Porch by campus to discuss this stuff.
- Podcasts: This semester I also discovered the joy of listening to good podcasts.
(1) Planet Money is the perfect length for my walk to/from campus, covers quirky stories loosely related to economics and finance, and includes a great episode with a shoutout to CMU’s Computer Science school.
(2) Talking Machines is a more academic podcast about Machine Learning. The hosts cover interesting recent ideas and hit a good balance—the material is presented deeply enough to interest me, but not so deeply I can’t follow it while out on a walk. The episodes usually explain a novel paper and link to it online, then answer a listener question, and end with an interview with a ML researcher or practitioner. They cover not only technical details, but other important perspectives as well: how do you write a ML textbook and get it published? how do you organize a conference to encourage women in ML? how do you run a successful research lab? Most of all, I love that they respect statisticians too 🙂 and in fact, when they interview the creator of The Automatic Statistician, they probe him on whether this isn’t just going to make the data-fishing problem worse.
(3) PolicyViz is a new podcast on data visualization, with somewhat of a focus on data and analyses for the public: government statistics, data journalism, etc. It’s run by Jon Schwabish, whom I (think I) got to meet when I still worked in DC, and whose visualization workshop materials are a great resource.
- It’s a chore to update R with all the zillion packages I have installed. I found that Tal Galili’s installr manages updates cleanly and helpfully.
- Next time I bake brownies, I’ll add some spices and call them “Chai squares.” But we must ask, of course: what size to cut them for optimal goodness of fit in the mouth?