Generalized additive models -- who does research on them besides Simon Wood? I use GAMs more and more.  When I go to provide references for their various components (smoothing parameter selection, various spline bases, p-values of smooth terms), they are all from one researcher -- Simon Wood, at the University of Bath, in England.  
He is also the maintainer of mgcv in R, which implements his body of work.  mgcv is enormously complex, but works remarkably well.  
There is older stuff, for sure.  The original idea is credited to Hastie & Tibshirani, and a great older textbook was written by Ruppert et al in 2003.
As an applied person, I don't have much of a feel for the zeitgeist among academic statisticians.  How is his work regarded?  Is it a bit strange that one researcher has done so much in one area?  Or is there other work that simply isn't noticed as much because it doesn't get put inside of mgcv?  I don't see GAMs used that much, though the material is reasonably accessible to people with statistical training, and the software is quite well-developed.  Is there much of a "back-story"?
Recommendations of perspectives pieces and other similar stuff from stat journals would be appreciated.
 A: There are many researchers on GAMs: it's just that basically the same model (GLM with linear predictor given by sum of smooth functions) is given lots of different names. You'll find models that you could refer to as GAMs called: semiparametric regression models, smoothing spline ANOVA models, structured additive regression models, generalized linear additive structure models, generalized additive models for location scale and shape, Gaussian latent variable models, etc. 
A small selection of researchers on GAM-related topics with a computational angle is: 
Ray Carroll, Maria Durban, Paul Eilers, Trevor Hastie, Chong Gu, Sonja Greven, Thomas Kneib, Stephan Lang, Brian Marx, Bob Rigby, David Ruppert, Harvard Rue, Fabian Scheipl, Mikis Stasinopoulus, Matt Wand, Grace Wahba, Thomas Yee.
(and there are a whole lot more people working on boosted GAMs, GAM-related theory and closely related functional data analysis methods). My papers are mostly about developing GAM methods that are efficient and general to compute with, but that's certainly not all there is to say on the subject.  
A: google scholar gives a lot of hits, in addition to the references above, and in comments, some which looks interesting is:
http://www.sciencedirect.com/science/article/pii/S0304380002002041     GAM's in studies of species distributions, published in "Ecological Modelling"
http://aje.oxfordjournals.org/content/156/3/193.short     Use of GAM's in studies of air pollution and health
but the OP seems to care more for statistical theory, so:  
http://www.sciencedirect.com/science/article/pii/S0167947398000334    this is about better fitting algorithms
http://onlinelibrary.wiley.com/doi/10.1111/1467-9876.00229/abstract   Bayesian inference based on MArkov Random Field priors
http://onlinelibrary.wiley.com/doi/10.1111/1467-9469.00333/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false     about estimation methods in GAM's ...
all this with many different authors, so the answer to original question seems to be many.
