I don't see such a sharp divergence as a computer science major working in a top statistics department. Statistics does not magically work well in hard sciences and machine learning has worked very well in many fields. I don't know if you consider biology hard science but biostatistics has many pitfalls many sciences fall into, and highly regarded statisticians can come to opposite conclusions on same data.
Statistical theory is actually applied probability, often called mathematical statistics. Math majors would feel right at home. In fact math majors look down on statistics major for lack of rigor and they have a point. Two years of classes in a five years phd program isn't enough to perfect knowledge of measure theory. But the more important problem is that stats training leave out computing enough that it impacts works coming out of stats department.
This gets back to difference between machine learning and statistics. Machine learning research embraces all fields of engineering, approximation and estimation, numerical analysis, optimization, plus statistics. Since so much of scientific advances can be attributed to computational improvements, it is natural that the more computational oriented fields are ahead of less computational fields. LASSO has been all the rage in statistics recently when it largely relied on works in convex programming. And signaling processing community in EE and CS are leagues ahead of statisticians in the sophistication and scales of problems they can tackle. Computational statistics is an attempt to remedy computational shortcoming of traditional statistics, but we have yet to see visible impact in term of high-impact work from statistics departments.
Having said all that, computer science departments do have the problem of not fully understanding the statistical foundation of machine learning methods. But this is not the case for CS in prestigious schools such as Berkeley and Stanford and MIT. Work coming out of these places are theoretically sound yet application oriented. One needs to just look at NIPs papers to appreciate the breadth and depth of expertise available in machine community.
For a good reading on bridging statistics and machine learning, read paper by an inventor of random forest, Leo Breiman, "Statistical Modeling: The Two Cultures." It is a well regarded paper by a renown probabilist and statistician who cares about utility of statistics as used in the real world.
Statistical theory is actually applied probability, often called mathematical statistics. Math majors would feel right at home. In fact math majors look down on statistics major for lack of rigor and they have a point. Two years of classes in a five years phd program isn't enough to perfect knowledge of measure theory. But the more important problem is that stats training leave out computing enough that it impacts works coming out of stats department.
This gets back to difference between machine learning and statistics. Machine learning research embraces all fields of engineering, approximation and estimation, numerical analysis, optimization, plus statistics. Since so much of scientific advances can be attributed to computational improvements, it is natural that the more computational oriented fields are ahead of less computational fields. LASSO has been all the rage in statistics recently when it largely relied on works in convex programming. And signaling processing community in EE and CS are leagues ahead of statisticians in the sophistication and scales of problems they can tackle. Computational statistics is an attempt to remedy computational shortcoming of traditional statistics, but we have yet to see visible impact in term of high-impact work from statistics departments.
Having said all that, computer science departments do have the problem of not fully understanding the statistical foundation of machine learning methods. But this is not the case for CS in prestigious schools such as Berkeley and Stanford and MIT. Work coming out of these places are theoretically sound yet application oriented. One needs to just look at NIPs papers to appreciate the breadth and depth of expertise available in machine community.
For a good reading on bridging statistics and machine learning, read paper by an inventor of random forest, Leo Breiman, "Statistical Modeling: The Two Cultures." It is a well regarded paper by a renown probabilist and statistician who cares about utility of statistics as used in the real world.