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> In industry, there exist quite some deep, interesting (e.g. math, programming) problems that (unluckily) many people in academia don't have on their radar. Th

> In industry, there exist quite some deep, interesting (e.g. math, programming) problems that (unluckily) many people in academia don't have on their radar. These kinds of problems often don't fit into the "boxes" of academic disciplines. Can you give some examples?

aleph_minus_one 1y ago
> Can you give some examples? I have reasons for being a bit cautious on giving details, but some hints on example areas are: - Understanding the dynamics (mathematics) of some exotic markets that are currently outside of the focus of investors. Very interesting mathematics is involved, but this is too "mathy" for many economists, and (currently, because the rules of the market dynamic still have to be sufficiently understood) too "vague" for many mathematicians who work in academia. - If you work on data integration problems and/or LoB business applications of some big, conservative companies, you begin to see that many of these problems are instances of deep abstract mathematical structures that are outside of the focus of the academic mathematicians who work in the respective academic area (think for example into the direction of algebraic geometry or algebraic topology): it is too "applied" for them. On the other hand, people in industry have a hate for people seeing these deep abstract patterns that could simplify the applications. - If you look deeply into some business calculations, you might think that the mathematics that is used there is "easy". But if thus some "theory" describing the business calculations does not need to describe "complicated" things that (academic) mathematicians love to think about - wouldn't this mean that there could exist a great abstraction that simplifies these business calculations a lot in computer programs? - If stochastics is used in, say, insurance industry in a much more "simple" way than in probability textbooks: couldn't there exist a much "simpler" (but likely very different) theory of stochastics that is sufficient to describe and solve the kind of questions that people in the respective industry care about (though for sure not the kind of questions that academic mathematicians who work in probability theory care about)? Again: people who work in industry hate employees who think about such questions. On the other hand, people in academia are interested in different questions. - If you look at the source code of some historically grown (but business-critical) business application, you begin to understand that there is no known data structure documented in academic literature that can describe all these things. So you start deriving it on your own. The problem is: business people don't like such deep thinking about the "correct data structure" for their business problems. On the other hand, academic computer scientists are not interested in this question either, because the things that the data structure describes is deeply intrtwined with how the business processes of the respective company work.

aleph_minus_one 1y ago

> Can you give some examples? I have reasons for being a bit cautious on giving details, but some hints on example areas are: - Understanding the dynamics (mathematics) of some exotic markets that are currently outside of the focus of investors. Very interesting mathematics is involved, but this is too "mathy" for many economists, and (currently, because the rules of the market dynamic still have to be sufficiently understood) too "vague" for many mathematicians who work in academia. - If you work on data integration problems and/or LoB business applications of some big, conservative companies, you begin to see that many of these problems are instances of deep abstract mathematical structures that are outside of the focus of the academic mathematicians who work in the respective academic area (think for example into the direction of algebraic geometry or algebraic topology): it is too "applied" for them. On the other hand, people in industry have a hate for people seeing these deep abstract patterns that could simplify the applications. - If you look deeply into some business calculations, you might think that the mathematics that is used there is "easy". But if thus some "theory" describing the business calculations does not need to describe "complicated" things that (academic) mathematicians love to think about - wouldn't this mean that there could exist a great abstraction that simplifies these business calculations a lot in computer programs? - If stochastics is used in, say, insurance industry in a much more "simple" way than in probability textbooks: couldn't there exist a much "simpler" (but likely very different) theory of stochastics that is sufficient to describe and solve the kind of questions that people in the respective industry care about (though for sure not the kind of questions that academic mathematicians who work in probability theory care about)? Again: people who work in industry hate employees who think about such questions. On the other hand, people in academia are interested in different questions. - If you look at the source code of some historically grown (but business-critical) business application, you begin to understand that there is no known data structure documented in academic literature that can describe all these things. So you start deriving it on your own. The problem is: business people don't like such deep thinking about the "correct data structure" for their business problems. On the other hand, academic computer scientists are not interested in this question either, because the things that the data structure describes is deeply intrtwined with how the business processes of the respective company work.