This month's issue of Amstat News features an editorial by Norman Matloff titled "Statistics Losing Ground to Computer Science." Provocative title, no?
I was expecting yet another article whose argument could be summed up as "get off of my lawn, you punk computer scientists!" When I read/hear these kinds of arguments from statisticians, I usually roll my eyes and move on with my life. But this time... I agreed.
Dr. Matloff's article is quite critical of CS research involving statistics. And maybe I'm getting crotchety, but I've run into many of these issues myself in my topic modeling research. An exemplar quote is below.
Due in part to the pressure for rapid publication and the lack of long-term commitment to research topics, most CS researchers in statistical issues have little knowledge of the statistics literature, and they seldom cite it. There is much “reinventing the wheel,” and many missed opportunities.
The fact of the matter is, CS and statistics come from very different places culturally. This doesn't always lend itself to clear communication and cross-disciplinary respect. Dr. Matloff touches on this mismatch. At one end...
CS people tend to have grand—and sometimes starry-eyed—ambitions. On the one hand, this is a huge plus, leading to highly impressive feats such as recognizing faces in a large crowd. But this mentality leads to an oversimplified view, with everything being viewed as a paradigm shift.
And at the other...
Statistics researchers should be much more aggressive in working on complex, large-scale, “messy” problems, such as the face recognition example cited earlier.
I 100% agree with the above. CS didn't start "overshadowing statistics researchers in their own field" simply because computer scientists "move fast and break things." In addition, our (statisticians') conservatism stifled creativity and ambitions to solve grand problems, like facial recognition (or text analyses).
Dr. Matloff recommends several changes for statistics to make. I particularly like the suggestion that more CS and statistics professors have joint appointments. A criticism that I regularly hear from my CS colleagues is that many statisticians are mediocre programmers, and that they lack pragmatism on the tradeoff between mathematical rigor and a useful application. We've covered CS's sometimes cavalier attitude towards modeling above. Perhaps more joint appointments will not only influence faculty, but also educate students early on the needs and advantages of both approaches.