Teaching-track positions at Washington University: a testimonial

My Ph.D. student Silas Johnson is teaching at Washington University in St. Louis. This is a kind of job that’s getting more and more popular; teaching-focused, non-tenurable but also not on a limited term. I’m pretty interested in the nuts and bolts of how these jobs work, so I asked Silas to explain it to me. Take it away, Silas! The rest of this post comes from him.

Washington University in St. Louis is hiring two new Lecturers this year: one in math, and one in statistics. These are long-term teaching-track positions, meaning they’re intended to be permanent but do not come with the possibility of tenure. I’m currently a Lecturer here, and I enjoy it a lot. I get to teach a lot of interesting courses; so far, my 3-3 load has usually included two sections of calculus and one upper-division course, with the latter including everything from probability to number theory. I also support the department’s broader undergraduate teaching mission. For example, since arriving, I’ve worked on a project to streamline the course requirements for our math major, tried out new ideas for calculus recitations, and conducted teaching interviews for postdoctoral candidates. Most importantly, my colleagues have been wonderfully supportive as I adjust to the university, try out new teaching methods and techniques in my courses, and work on departmental projects.

These teaching-track faculty positions seem to be increasingly popular, though the nature and details of such positions vary from department to department. While non-tenured, my position is on a parallel promotion ladder from Lecturer to Senior Lecturer to Teaching Professor. This structure is fairly typical, though the titles vary (Assistant/Associate/Full Professor of Instruction is also common). In our case, the promotions also come with increased guarantees of job security.

Teaching-focused positions are sometimes stereotyped as a lesser option, perhaps even a backup plan for those who can’t find a research postdoc or tenure-track job. I disagree; I see this as a good path for mathematicians who, like me, have a genuine interest in teaching and want to make it the focus of their careers. Our department and college are clear about the value they place on teaching-track faculty, too; we vote in department meetings, serve on important committees, and are treated as equals in just about every way. (The only thing we can’t do is vote on tenure-track hiring and promotion.)

Overall, I really like it here. I’m happy with my decision to pursue a teaching career, and I’m glad there are other mathematicians out there who are interested in doing the same. I would encourage such people to apply for our position and others like it. If you’re a grad student, by the way, there are teaching-focused postdoc positions too!

“The Great Ph.D. Scam” (or: Academy Plight Song)

Thanks to the Wayback Machine, here’s my piece from the Boston Phoenix on the MLA, the first feature piece I ever wrote for publication, twenty-one years ago last month.

Who knows if the Wayback Machine is forever?  Just in case, I’m including the text of the piece here.

The Phoenix gave this piece its title, which I think is too fighty.  My title was “Academy Plight Song.”  (Get it?)

I think this holds up pretty well!  (Except if I were writing this today I wouldn’t attach so much physical description to every woman with a speaking part.)

Melani McAlister, the new hire at GWU who appears in the opening scene, is still there as a tenured professor in 2018.  And all these years later, she’s still interested in helping fledgling academics navigate the world of scholarly work; her page “Thinking Twice about Grad School” is thorough, honest, humane, and just great.

Here’s the piece!

The great PhD scam
by Jordan Ellenberg

“We dangle our three magic letters before the eyes of these predestined victims, and they swarm to us like moths to an electric light. They come at a time of life when failure can no longer be repaired easily and when the wounds it leaves are permanent . . . ”
— William James
“The Ph.D. Octopus,” 1903

By nine o’clock, more than 200 would-be professors have piled into the Cotillion Ballroom South at the Sheraton Washington hotel, filling every seat and spilling over into the standing space behind the chairs. They’re young and old, dressed up and down, black and white and other (though mostly white). They’re here to watch Melani McAlister, a 1996 PhD in American Civilization from Brown, explain to a committee of five tenured professors why she ought to be hired at Indiana University.

Everybody looks nervous except McAlister. That’s because, unlike almost everyone else here, she doesn’t need a job; she’s an assistant professor at George Washington University. This interview is a mock-up, a performance put on to inform and reassure the crowd of job-seekers. As McAlister cleanly fields questions about her thesis and her pedagogical strategy, the people in the audience frown and nod, as if mentally rehearsing their own answers to the similar questions they’ll be asked in days to come.

This is night one of the 112th annual meeting of the Modern Language Association, the national organization of professors of English, comparative literature, and living foreign languages. Ten thousand scholars are here in Washington, DC, to attend panels, renew acquaintances, and, most important, to fill open faculty positions. A tenure-track job typically attracts hundreds of applicants; of these, perhaps a dozen will be offered interviews at the MLA; and from that set a handful will be called back for on-campus interviews. For the people who are here “on the market,” that is, trying to become professors of English and so forth, the MLA is the gate to heaven. And, as everyone in the room is aware, the gate is swinging shut.

Is academia wrong for you?

Good article by Daniel McCormack in Chronicle of Higher Education on underpublicized aspects of academic life.

For instance:

These iterative failures are, at a very deep level, the essence of creating new knowledge, and are therefore inseparable from the job. If you can’t imagine going to bed at the end of nearly every day with a nagging feeling that you could have done better, academe is not for you.

The academic workplace is a really unusual one.  For instance, it’s one of the few places to work where you’re nobody’s boss and nobody’s your boss.  It really suits some people — I’m one.  But lots of other people feel otherwise: it’s too slow, too lacking in immediate feedback, too content with the way “it’s always been done.”  And a lot of those people have great things to contribute to mathematics and don’t fit in the system we’ve set up to pay people to do math.

Also, this:

So while the ideal career path leads from graduate school to a tenure-track position, the one you will more likely find yourself on leads from graduate school to a series of short-term positions that will require you to move — often.

is less true in math than in many other areas, but still kind of true.  And it works badly not just for people who temperamentally hate moving, but for people who want to have kids and have a limited childbearing window.

McCormack is right:  without catastrophizing, we should always be trying to give our Ph.D. students as real a picture as possible of what academic life is like, and not just the advisor’s life with tenure at an R1 university.  Lots of people will still happily sign up.  But other people will think more seriously about other great ways to do mathematics.

 

 

 

Mathematicians becoming data scientists: Should you? How to?

I was talking the other day with a former student at UW, Sarah Rich, who’s done degrees in both math and CS and then went off to Twitter.  I asked her:  so what would you say to a math Ph.D. student who was wondering whether they would like being a data scientist in the tech industry?  How would you know whether you might find that kind of work enjoyable?  And if you did decide to pursue it, what’s the strategy for making yourself a good job candidate?

Sarah exceeded my expectations by miles and wrote the following extremely informative and thorough tip sheet, which she’s given me permission to share.  Take it away, Sarah!

 

 

Women in math: accountability

I’ve talked about women in math a lot on this blog and maybe you think of me as someone who is aware of and resistant to sexism in our profession.  But what if we look at some actual numbers?

My Ph.D. students:  2 out of 15 are women.

Coauthors, last 5 years: 2 out of 23 are women.

Letters posted on MathJobs, last 2 years:  3 out of 24 are women.

That is sobering.  I’m hesitant about posting this, but I think it’s a good idea for senior people to look at their own numbers and get some sense of how much they’re actually doing to support early-career women in the profession.

Update:  I removed the numbers for tenure/promotion letters.  A correspondent pointed out that these, unlike the other items, are supposed to be confidential, and given the small numbers are at least partially de-anonymizable.

Ranking mathematicians by hinge loss

As I mentioned, I’m reading Ph.D. admission files.  Each file is read by two committee members and thus each file has two numerical scores.

How to put all this information together into a preliminary ranking?

The traditional way is to assign to each applicant their mean score.  But there’s a problem: different raters have different scales.  My 7 might be your 5.

You could just normalize the scores by subtracting that rater’s overall mean.  But that’s problematic too.  What if one rater actually happens to have looked at stronger files?  Or even if not:  what if the relation between rater A’s scale and rater B’s scale isn’t linear?  Maybe, for instance, rater A gives everyone she doesn’t think should get in a 0, while rater A uses a range of low scores to express the same opinion, depending on just how unsuitable the candidate seems.

Here’s what I did last year.  If (r,a,a’) is a triple with r is a rater and a and a’ are two applicants, such that r rated a higher than a’, you can think of that as a judgment that a is more admittable than a’.  And you can put all those judgments from all the raters in a big bag, and then see if you can find a ranking of the applicants (or, if you like, a real-valued function f on the applicants) such that, for every judgment a > a’, we have f(a) > f(a’).

Of course, this might not be possible — two raters might disagree!  Or there might be more complicated incompatibilities generated by multiple raters.  Still, you can ask:  what if I tried to minimize the number of “mistakes”, i.e. the number of judgments in your bag that your choice of ranking contradicts?

Well, you can ask that, but you may not get an answer, because that’s a highly non-convex minimization problem, and is as far as we know completely intractable.

But here’s a way out, or at least a way part of the way out — we can use a convex relaxation.  Set it up this way.  Let V be the space of real-valued functions on applicants.  For each judgment j, let mistake_j(f) be the step function

mistake_j(f) = 1 if f(a) < f(a’) + 1

mistake_j(f) = 0 if f(a) >= f(a’) + 1

Then “minimize total number of mistakes” is the problem of minimizing

M = sum_j mistake_j(f)

over V.  And M is terribly nonconvex.  If you try to gradient-descend (e.g. start with a random ranking and then switch two adjacent applicants whenever doing so reduces the total number of mistakes) you are likely to get caught in a local minimum that’s far from optimal.  (Or at least that can happen; whether this typically actually happens in practice, I haven’t checked!)

So here’s the move:  replace mistake_j(f) with a function that’s “close enough,” but is convex.  It acts as a sort of tractable proxy for the optimization you’re actually after.  The customary choice here is the hinge loss:

hinge_j(f) = min(0, f(a)-f(a’) -1).

Then H := sum_j hinge_j(f) is a convex function on f, which you can easily minimize in Matlab or python.  If you can actually find an f with H(f) = 0, you’ve found a ranking which agrees with every judgment in your bag.  Usually you can’t, but that’s OK!  You’ve very quickly found a function H which does a decent job aggregating the committee scores. and which you can use as your starting point.

Now here’s a paper by Nihal Shah and Martin Wainwright commenter Dustin Mixon linked in my last ranking post.  It suggests doing something much simpler:  using a linear function as a proxy for mistake_j.  What this amounts to is:  score each applicant by the number of times they were placed above another applicant.  Should I be doing this instead?  My first instinct is no.  It looks like Shah and Wainwright assume that each pair of applicants is equally likely to be compared; I think I don’t want to assume that, and I think (but correct me if I’m wrong!) the optimality they get may not be robust to that?

Anyway, all thoughts on this question — or suggestions as to something totally different I could be doing — welcome, of course.

 

 

 

Ranking mathematicians

I’m on the hiring committee, I chair the graduate admissions committee, and I’m doing an NSF panel, so basically I’ll be spending much of this month judging and ranking people’s mathematics.  There’s a lot I like about these jobs:  it’s a very efficient way to get a panorama of what’s going on in math and what people think about it.  The actual ranking part I don’t like that much — especially because the nature of hiring, admissions, and grant-making means you’re inevitably putting tons of very worthwhile stuff below the line.  I feel like a researcher when I read the proposals, like a bureaucrat when I put scores on them.

But of course the bureaucratic work needs to be done.  I’d go so far as to say — if mathematicians aren’t willing to rank each other, others will rank us, and that would be worse.

Doctoral programs can have a strong influence on the weak-minded

Daniel Drezner:

First, I cannot stress enough the cult-like powers of a PhD program. Doctoral programs can have a strong influence on the weak-minded. Even if you’re pretty sure what you want going into a program, that can change as you’re surrounded by peers who want something different. You might think you’re strong-willed, but day after day of hearing how a top-tier research university position is the be-all, end-all of life can have strange effects on your psyche.

I really do feel this is something we handle well at Wisconsin.  Our Ph.D. graduates go on to a wide variety of positions, some in primarily teaching colleges, some in research institutions, some in industry, some in government.  We do not consider the North American research university the be-all and end-all of life.  We are not just trying to produce clones of ourselves.  We really do strive to help each of our students get the best job among the jobs they want to get.  

Math Bracket 2015

March Math Madness is here!  Presenting the 2015 math bracket, as usual prepared by our crack team of handicappers here at the UW math department.  As always, remember that the math bracket is for entertainment purposes only and you should not take offense if the group rated your department lower than the plainly inferior department that knocked you out.  Under no circumstances should you use the math bracket to decide where to go to grad school.

Lots of tough choices this year!

 

Are UW-Madison professors underpaid?

It’s well known that UW-Madison salaries are notably lower than those at peer institutions, at every level of seniority.  But wait, says Chris Rickert in the Wisconsin State Journal, that doesn’t necessarily mean our faculty is underpaid!

At UW-Madison, assistants are paid, on average, about $82,000 a year, associates about $93,000 and full professors about $123,000 — ranking them 10th, seventh and 12th, respectively, in salary compared to 11 other state-identified peer institutions, according to data from the university’s Academic Planning and Institutional Research office.

Obviously, more full professors means more people in line for full-professor salaries and greater pressure on the budget for professorial salaries overall. At UW-Madison, that’s no small detail, as about 59 percent of UW-Madison professors have attained full status, according to the university’s Data Digest.

By contrast, figures from the American Association of University Professors show that, on average, only about 31.5 percent profs at all universities and about 30.8 percent at public universities are full professors.

This is a really good point!  You could imagine that maybe our pay isn’t underscale at all — maybe we just promote people faster, so that our full professors are less senior and thus make less.  That’s Rickert’s take:

I’m left to wonder whether the university has adopted that old human resources trick of placating employees by inflating their titles more than their pay.

In an era of declining state support, this would help keep a lid on the cost of higher education while simultaneously allowing university officials to complain about how poorly paid are its best and brightest.

But this can actually be checked!  You can use the Chronicle of Higher Education Faculty Salary Survey to get the mean salary for any university at any seniority level, and the number of faculty members at each seniority level, and compute the overall faculty mean that way.  I did this for a few of our peer institutions and got:

	UIUC		UW		Iowa		OSU
full	$145	816	$123	755	$135	494	$139	943
assoc	$96	556	$93	252	$90	407	$94	763
asst	$90	405	$82	363	$77	329	$85	502
	$117,133		$106,618		$104,595		$111,172

So the mean UW tenure-track gets paid slightly more than people at Iowa, but notably less than counterparts at Ohio State and Illinois.