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68 Statistics before and after COPSS Prizewhich showed how difficult implementation of particle filters is in the atmosphericsciences (Snyder et al., 2008). The work in HMM came up again inthe context of traffic forecasting (Bickel et al., 2007) and some work in astrophysics(Meinshausen et al., 2009). Both papers were close collaborationswith John Rice and the second included Nicolai Meinshausen as well. Theearly bootstrap work with Freedman eventually morphed into work on the mout of n bootstrap with Götze and van Zwet (Bickel et al., 1997) and finallyinto the Genome Structural Correction Method (Bickel et al., 2010).Another quite unrelated observation is that to succeed in applications onehas to work closely with respected practitioners in the field. The main reasonfor this is that otherwise, statistical (and other mathematical science)contributions are dismissed because they miss what practitioners know is theessential difficulty. A more pedestrian reason is that without the imprimaturand ability to translate of a respected scientist in the field of application, statisticalpapers will not be accepted in the major journals of the science andhence ignored.Another observation is that high-order computing skills are necessary tosuccessfully work with scientists on big data. From a theoretical point of view,the utility of procedures requires not only their statistical, but to an equalextent, their computational efficiency. Performance has to be judged throughsimulations as well as asymptotic approximations.IfreelyconfessthatIhavenotsubscribedtotheprincipleofhoningmyowncomputational skills. As a member of an older generation, I rely on youngerstudents and collaborators for help with this. But for people starting theircareers it is essential. The greater the facility with computing, in addition toR and including Matlab, C++, Python or their future versions, the better youwill succeed as a statistician in most directions.As I noted before, successful collaboration requires the ability to reallyunderstand the issues the scientist faces. This can certainly be facilitated bydirect study in the field of application.And then, at least in my own career, I’ve found the more mathematicsIknew,fromprobabilitytofunctionalanalysistodiscretemathematics,thebetter. And it would have been very useful to have learned more informationtheory, statistical physics, etc., etc.Of course I’m describing learning beyond what can be done or is desirablein a lifetime. (Perhaps with the exception of John von Neumann!) We all specializein some way. But I think it’s important to keep in mind that statisticsshould be viewed as broadly as possible and that we should glory in this timewhen statistical thinking pervades almost every field of endeavor. It is reallyalotoffun.