interdisciplinary research

I spent some time this afternoon discussing possibilities for interdisciplinary research with Rob Fergus (NYU) in the Computer Science Department. We decided that there are many points of intellectual overlap, especially that we work on images, that we work on brute-force methods with enormous numbers of images (and large amounts of disk), that we can't trust the sources of our data and we face unreliable meta-data, that we need methods that scale to problems where the number of images is in the billions (that is, we need better than linear scaling), that we are trying to build models of the image universe directly from the data and not from a heavy theoretical or conceptual framework, and that we want to make systems that just work, like the web services of Astrometry.net.

The differences, of course, are that my problems are very specific while Fergus's are very general. His problems are harder, in this sense. On the other hand, the precision and accuracy and false-positive constraints on my problems are much more severe. So there are significant differences in what we are doing. The challenge is to build an interdisciplinary program that benefits from the overlap, but produces useful output in both scientific domains.