that we really need the serious thought, the serious integration, multidisciplinary collaboration, to be developing the methods, overseeing the methodological development, as well as being able to communicate back to the public what is going on here. So, I thought that was kind of interesting. So, collaboration, there needs to be very close collaboration in areas like systems engineering, hardware software design, statistics, mathematics, computer science database type things, and basic science. That has to come together. Now, that is not easy because, again, we have been saying that forever that this is how we are going to solve these problems.
Then that comes into play, what are the mechanisms that we can try to do that? We didn’t have a lot of good answers there. One idea was, is it possible to mount certain competitions that really are getting at serious fusion of information that would require multidisciplinary teams like this to come together. There was a suggestion that, at some of our national institutes, such as SAMSI, that is Science and Applied Mathematics Institute, one of the new, not solely NSF-funded, but one of the new NSF-funded institutes, perhaps some sort of a focus here. I think that gets back to Doug’s comment, which I thought was really good, that regular meetings as opposed to one up workshops is the way we are probably going to foster relationships between these communities. Clearly, funding is required for those sorts of things. Can we get funding agencies to require collaborations, and how do they then monitor and mediate how that happens.
Then, one comment that was made at the end was the fact that, if we just focus in on statistics, and statistics graduate training, there is a lot of question as to whether we are actually training our students such that they can really begin to bite off these problems. I mean, do they have the computational skills necessary and the ability to do the collaborations. I think that is a big question. My answer would be, I think in some of our programs we are, and in others we are not, and how do we balance that?
Just one last comment. You know, we spoke at very high level and just at the end of our time—and then we sort of ran out of time—it was pointed out that if you really think of a data mining area and data mining problems, that there has been a lot done on supervised and unsupervised learning. I think we understand pretty well that these are methods that have good predictive capabilities. However, it seems that the problem of the day is anomaly detection, and I really think that there, from a data fusion point of view, we really have a dearth of what we know how to do. So, the ground is fertile, the problems are hard, and somehow we have got to keep the dialogue going.