Home > data science, math, statistics > How math departments hire faculty

How math departments hire faculty

December 7, 2012

I just got back from a stimulating trip to Stony Brook to give the math colloquium there. I had a great time thanks to my gracious host Jason Starr (this guy, not this guy), and besides giving my talk (which I will give again in San Diego at the joint meetings next month) I enjoyed two conversations about the field of math which I think could be turned into data science projects. Maybe Ph.D. theses or something.

First, a system for deciding whether a paper on the arXiv is “good.” I will post about that on another day because it’s actually pretty involved and possible important.

Second is the way people hire in math departments. This conversation will generalize to other departments, some more than others.

So first of all, I want to think about how the hiring process actually works. There are people who look at folders of applicants, say for tenure-track jobs. Since math is a pretty disjointed field, a majority of the folders will only be understood well enough for evaluation purposes by a few people in the department.

So in other words, the department naturally splits into clusters more or less along field lines: there are the number theorists and then there are the algebraic geometers and then there are the low-dimensional topologists, say.

Each group of people reads the folders from the field or fields that they have enough expertise in to understand. Then from among those they choose some they want to go to bat for. It becomes a political battle, where each group tries to convince the other groups that their candidates are more qualified. But of course it’s really hard to know who’s telling the honest truth. There are probably lots of biases in play too, so people could be overstating their cases unconsciously.

Some potential problems with this system:

  1. if you are applying to a department where nobody is in your field, nobody will read your folder, and nobody will go to bat for you, even if you are really great. An exaggeration but kinda true.
  2. in order to be convincing that “your guy is the best applicant,” people use things like who the advisor is or which grad school this person went to more than the underlying mathematical content.
  3. if your department grows over time, this tends to mean that you get bigger clusters rather than more clusters. So if you never had a number theorist, you tend to never get one, even if you get more positions. This is a problem for grad students who want to become number theorists, but that probably isn’t enough to affect the politics of hiring.

So here’s my data science plan: test the above hypotheses. I said them because I think they are probably true, but it would be not be impossible to create the dataset to test them thoroughly and measure the effects.

The easiest and most direct one to test is the third: cluster departments by subject by linking the people with their published or arXiv’ed papers. Watch the department change over time and see how the clusters change and grow versus how it might happen randomly. Easy peasy lemon squeazy if you have lots of data. Start collecting it now!

The first two are harder but could be related to the project of ranking papers. In other words, you have to define “is really great” to do this. It won’t mean you can say with confidence that X should have gotten a job at University Y, but it would mean you could say that if X’s subject isn’t represented in University Y’s clusters, then X’s chances of getting a job there, all other things being equal, is diminished by Z% on average. Something like that.

There are of course good things about the clustering. For example, it’s not that much fun to be the only person representing a field in your department. I’m not actually passing judgment on this fact, and I’m also not suggesting a way to avoid it (if it should be avoided).

Categories: data science, math, statistics
  1. December 7, 2012 at 10:41 am | #1

    The model of faculty recruitment into specialties you propose sounds a lot like a Chinese Restaurant Process. Bigger groups have more strength and are capable of grabbing more of the new recruits.

    http://en.wikipedia.org/wiki/Chinese_restaurant_process

  2. mathematrucker
    December 7, 2012 at 10:54 am | #2

    The title of Cathy’s talk is “Weapons of Math Destruction” and the name of my new website is “Math Transit.com”. For anyone old enough to remember the Coneheads, “Math Quantities” might ring a bell. Perhaps Cathy’s talk will refer to some specific “Math Murderers”. (I briefly tried doing something with e=mc^2 but nothing came to mind. There must be more.)

    • mathematrucker
      December 7, 2012 at 11:03 pm | #3

      Also kind of like “Critical Math” and “Math Hysteria”. There must surely be more.

  3. JSE
    December 7, 2012 at 10:58 am | #4

    But of course departments also say things like “We have a pressing need in field X, where we really don’t have anyone — let’s hire Y.” Or: “Let us not hire Z, who’s so close in area to our current faculty members W and Y that the hire would be somewhat redundant.” The question is, are the clustering effects or the declustering effects stronger?

    Probably the relative strength is different in different regimes. E.G. one think you DON’T see (at least as far as I can think of) is departments which entirely composed of representatives of a single area.

    • Thads
      December 7, 2012 at 12:04 pm | #5

      A narrowly focused math department (say of number theory only) could provide an amazing research atmosphere. But at some point, the administration tends to step in and object. They prefer an even distribution of specialties, which for pedagogical reasons is necessary in other subjects, and desirable even in math.

    • December 7, 2012 at 8:47 pm | #6

      My personal experience is that the political game actually favors declustering for the following three reasons. First, if two or more clusters can find someone halfway in between then they can combine their political weight to push that candidate. Second, the endorsement of a candidate at the edge of one’s own interests is inherently more credible since it perceived as less likely to be the result of self-interest. Third, candidates very close to existing faculty often look poor in comparison if only because they’re younger and hence less proven.

      There are a handful of departments that do choose explicitly to focus on just a few areas; for instance Boston College is heavily concentrated in low-dimensional topology/geometry, number theory, and algebraic geometry.

  4. December 7, 2012 at 11:06 am | #7

    I’d sure like to hear about any elephantresses in the room?

  5. mathematrucker
    December 7, 2012 at 11:17 am | #8

    Grad student selection is a somewhat related area that I would guess data science has a good chance of improving, too.

  6. December 7, 2012 at 11:34 am | #9

    I wish to relate in some detail an experience as a semi-insider I had around 2002. I was considering teaching math at the Collegiate level (somewhere between American high school and 4-year colleges) in Quebec, which has a unique in
    US/Canada states or provinces system called CEGEPs. So, I had to take a one full-year Certificate of Education at the CEGEP Level at University within the University’s Faculty of Education. The course work was extremely boring to me, for 80% of the classes. Then, there came the Practicum. I was paired-up with a CEGEP teacher, assisted his lectures and so forth. One day, I was invited to attend a “Big Meeting”. There were about 30-40 people there. The Collegiate Institute had decided to go-ahead with a new Technical program. CEGEPs act both as pre-University in Quebec (2 years, then 3 years for University B.Sc., except
    4 years in Engineering), and as the final step before qualification for Technicians and nurses. As far as I know, the Technical (non-University-bound) programs were always 3 years long. Going back to the “Big Meeting”, there was a faction from the Technical lecturers that wanted more “units” of Course-work in the Technical side of Lecturers, and an opposing faction from the College Mathematics Department that wanted more “units” of course-work given by the Math Department. To make things worse, the steering committee that deliberated on the Curriculum for this future (planned) Technique was composed largely of Lecturers from Technical with an “interest” and Lecturers from Math also with an “interest”. Progress in-committe was pretty much stalled. The Academic Vice-Chancellor (so to speak), who chaired the “Big Meeting” put forward the concept-idea of adding an “observation” or “feedback-committee” that would attend and listen to the deliberations of the “steering committee”. The math dept. lecturers were against instituting a “feedback-committee” to the General Assembly, and that seemed to be the Lecturer-sense, generally speaking. Then the Union-Head took the foor brandishing the collective bargaining agreement and asserting that the Academic Vice-Chancellor was over-stepping his true mandate, or something similar. The Academic Vice-Chancellor said essentially that he would not impose a “feedback committtee”, but that from previous experience, in case of stalemate in the “steering committee”, he would be obliged to “adjudicate” on the contentious Curriculum question for the future new Technical Program. So, I was there about two hours, and then I had had enough. I think my mentor from Math Dept. stayed. But I went home.
    So, I guess turf-wars can happen within faculties, colleges and as I explained here,
    in the process of establishing the curriculum for a new Technical Program in some Quebec Colleges (CEGEPs).

  7. December 7, 2012 at 1:19 pm | #10

    I’m not sure I buy your implicit assumption that it would be better for departments to get faculty in more areas instead of picking a set of disciplines to specialize in. I recall Brown’s job listings being very explicit about their view on this, saying something like “While applicants from all areas will be considered, we are particularly interested in those with interests consonant with current members of the department.”

    Certainly as a department grows substantially, it will eventually want faculty in new fields. But that can happen organically, as existing members discover new interests later in their careers.

  8. David Austin
  9. Dan L
    December 7, 2012 at 3:17 pm | #13

    My limited understanding of how math departments choose to hire suggests that there is actually a lot of diversity in how different departments hire. Some seem to want to hire to their strength, while others seem to want to hire to their weaknesses. Others might be legitimately interested in the “best applicant” regardless of field. But in any event, the fundamental problem is that it is really hard to compare researchers in different subfields. And that’s before you even consider the even thornier question of the relative merits of entire research areas. At the end of the day, I think that diversity of opinion is crucial. I think it would be a bad thing if all departments agreed on a single “right” approach to hiring. As long as a department cab stay true to its mission without becoming consumed by politics, they can’t really go too wrong.

  10. Jason Starr
    December 7, 2012 at 5:34 pm | #14

    Your colloquium was fantastic!

  11. Deane
    December 7, 2012 at 10:41 pm | #15

    In my experience it is extremely difficult for any smallish not-top-ranked department to attract an honestly good mathematician outside the existing strengths of the department. And even if you succeed somehow, say because you find someone good but somehow undiscovered or underappreciated, you often end up losing that person later for one reason or another. It is often better to focus and build on existing strengths, instead of trying to broaden the academic diversity of the department. One should of course look for unexpected opportunities to create new strengths, say because someone really good needs for some reason to move to the local area. But it’s hard to purposely build in a new area without such an accident.

  12. December 8, 2012 at 1:42 am | #16

    I have a question for Cathy. You seem to be of the opinion that more clusters per department would be better, in some sense. But applicants to grad school are (up to a appoint) “free agents”. They can apply where good advisers are, except that location and cost of education also matter. So, if more clusters is better, in what sense? by what “metric” ?

    • December 8, 2012 at 9:52 am | #17

      Here’s one reason that more clusters are better from the point of view of grad school applicants. Lots of prospective graduate students in math (especially the US ones) have no idea what area they’re going to specialize in, and hence a broader department is inherently more attractive as a place to go to graduate school.

  13. April 29, 2013 at 7:25 pm | #18

    Sorry for the late comment, but I only recently started following your blog regularly, and followed a link back to this post.

    Something similar to the ArXiv paper rater that your describe has been implemented by popular vote for quant-ph via SciRate, and automatically in some other fields like toxicogenomics, I discuss the latter alongside some wild speculation, here.

  14. September 25, 2013 at 11:15 pm | #19

    First, a system for deciding whether a paper on the arXiv is “good.” I will post about that on another day because it’s actually pretty involved and possible important.

    From googling it doesn’t look like you’ve written this yet. If/when you do, would you mind tweeting @isomorphisms to let me know?

  15. September 25, 2013 at 11:22 pm | #21
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