Building a statistics department

Aleks sent me these slides by Jan de Leeuw describing the formation of the UCLA statistics department. Probably not much interest unless you’re a grad student or professor somewhere, but it’s fascinating to me, partly because I know the people involved and partly because I admire the UCLA stat dept’s focus on applied and computational statitics. In particular, they divide the curriculum into “Theoretical”, “Applied”, and “Computational”. I think that’s about right, and, to me, much better than the Berkeley-style division into “Probability”, “Theoretical”, and “Applied”. Part of this is that you make do with what you have (Berkely has lots of probabilists, UCLA has lots of ocmputational people) but I think that it’s a better fit to how statistics is actually practice.

It’s also interesting that much of their teaching is done by continuing lecturers and senior lecturers, not by visitors, adjuncts, and students. I’m not sure what to think about this. One of the difficulties with hiring lecturers is that the hiring and evaluation itself should be taken seriously, which really means that experienced teachers should be doing the evaluation. So I imagine that getting this started could be a challenge.

I also like the last few slides, on Research:

Centers

We went from a mathematics model (one faculty member in an office with pencils and yellow pads) to a science model (one or more faculty members with graduate students in labs). Centers are autonomous, support their graduate students, and are associated with specialized courses.

— Center for Applied Statistics (Berk, Schoenberg, De Leeuw)

— Center for Statistical Computing (Hansen)

— Center for Image and Vision Sciences (Zhu, Yuille, Wu)

— Center for the Teaching of Statistics (Gould and the Teaching Faculty)

— Laboratory of Statistical Genomics (Sabatti)

— Studio of Bio-data Refining and Dimension Reduction (Li)

Lessons

— Pay attention to Campus Initiatives (Bioinformatics, Computing, UCLA in LA).

— Link with large interdisciplinary projects (Embedded Networks, Institute for the Environment).

— PI’s autonomy and reponsibility. Federal model.

— Use a very wide definition of statistics.

— Preprints, Digital Library, E-Journals.

I think this approach could work well even with a Berkeley-type department that is strong on probability. I like the idea of offering opportunities to students rather then telling them all what to do.