Top Hackers, we need you for Operations Research

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).<p>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.<p>People from computer science departments have also been gradually moving into these areas, witness growth in machine learning, algorithmic game theory etc.

I would argue that the cross pollination is happening already. In my Convex Optimization grad course, three fourth of us were CS/EE majors (the majority being C.S.). The material is not that hard to pick up if you have a background in Linear Algebra and Probability which if you are doing research in Machine Learning, you have already picked up.<p>Depending on the specifics of the problem, I would assert that it is possible for a more traditional OR trained person (who is rapidly disappearing IMO) to beat the CS person. Software Engineering tells you to modularize your code, use meaningful variable names etc. Typically Matlab codes written for OR problems try to replicate the same variable names used in deriving out the analytic equations (e.g. v, \lambda, \eta etc). Also, there is a strong emphasis on trying to make the code as compact as possible at the cost of modularity as a way of ensuring that mistakes are minimized. I have learned from painful experience (17 hours of debugging :) that the latter way of thinking is not wrong just different and even makes sense in specific domain related problems.

An ORite won't really make a difference if the approach is already chosen (e.g. "write a tabu search for the VRP"). She does make a difference when the problem is new, when looking for problem features (e.g. symmetry, decomposability, extreme points of the set of solutions), when deciding what approach to use (size matters).<p>Given an unsolved OR problem, a top 10% ORite will likely beat a top 1% hacker (or even CS person) in solving that problem.

Don't write off CS grad students who did most of their research in OR. My CS dissertation was in an area that would definitely be considered OR, and many CS students at our school learned Tabu search, Simulated Annealing, etc., often in the context of machine learning or local search algorithms.<p>I would have guessed that most OR research labs had a fair number of CS grad students.

The problem I see is, that it is really difficult to understand the OR sphere from the outside. While one can identify the need for some regular software product (App, etc) by just watching and participating actively in the market, this seems almost impossible in OR.

Where do you get an OR job? What would be the top OR employer?