Hi Adam, 

 working on other parts of the library as well would be great but for now I need to focus on the R-Tree (time constraints prohibit doing otherwise). I believe beginning with bug fixes and studying is the way to learn. Once I acquire sufficient knowledge of the library (~month considering that summer holidays are upon us) I'd like to work on balancing/packing algorithms. Also serialization and support for external memory algorithms is an interesting topic, but I believe I'll have a better insight only after the ~month learning period. 

 As for things related to my thesis, if there's a plan to incorporate GPU computing to the library (especially now that "boost.compute" seems to be able to make it into boost) I'd be happy to work on implementations and propose algorithms there as well; I attached a small presentation-survey project (skip to section 4, the initial part is just introduction to RTrees) where you can see areas of application for GPUs and references to some papers on the topic.  

 I'll be expecting guideliness on how to work on bug fixes (or priorities therein - hopefully sorted by ascending difficulty :P )