|
|
|
|
|
|
by dvse
5093 days ago
|
|
|
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. |
|
|
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.