OR/CS is not the ultimate combination. If you want to do interesting work in this space you ideally need a background in all of optimization (ILP, convex NLP, stochastic optimization),control theory, certain areas of economics (general equilibrium, game theory, mechanism design, concepts behind applied finance), statistics/econometrics (out of sample performance, hypothesis testing, causality, dealing with non random samples) and probability (mainly stochastic processes).
OR itself contains a large number of applications that combine many of the above, e.g. network revenue management, but someone who has taken grad courses from the OR department alone would genuinely struggle to do anything significantly new or interesting.
People from computer science departments have also been gradually moving into these areas, witness growth in machine learning, algorithmic game theory etc.
I actually agree with you there. Since OR is so multi-disciplinary, it is paramount to have a broad knowledge of all the fields it touches. Although I specialised in OR in my masters, my bachelors was actually CS&Economics/Business, which has been of great value to me.
In terms of CS moving into machine learning and artificial intelligence, the focus tends to be on applications in the consumer sphere - e.g. analysing big data to understand and recommend to consumers ala Amazon/Netflix, or image recognition to self-driving cars.
But these are mere sub-domains of OR. In business, what I think has the highest value and remains yet unexploited is the optimization branch/sub-topic of OR. The likes of production optimization, supply chain optmization, inventory optimization, facility location optimization, and my favorite, vehicle routing optimization.
Just for fun - "stochastic optimization" as in here:
www2.isye.gatech.edu/~ashapiro/download.php?Down=book
is in fact identical to "empirical risk minimization" in Vapnik's statistical learning theory. As soon as you are not 100% sure about any of the numbers in your routing model, you find yourself in the same setting as that studied in "machine learning"!
OR itself contains a large number of applications that combine many of the above, e.g. network revenue management, but someone who has taken grad courses from the OR department alone would genuinely struggle to do anything significantly new or interesting.
People from computer science departments have also been gradually moving into these areas, witness growth in machine learning, algorithmic game theory etc.