I am a somewhat new-ish member of Kaggle competitions with 1 submission. Do academics research via Kaggle challenges/competitions? If so are there any prominent names?
If not why? They provide well annotated training data-sets, and typically do not put any publication restrictions as well.
Perhaps they consider such challenges trivial. Just a query on an academic's perspective on the challenges here.
The only thing I've heard about Kaggle from an academic's perspective is from Graduate School Students at Stanford, who need to participate in a challenge as a part of their coursework (which is pretty cool).
Answer
The main reason "Academics" wouldn't browse Kaggle to search for research problems is that most academics already have a field of interest, and aren't interested in doing data science research for the sake of figuring out some random problem defined by whoever decided to post the contest. Even if an algorithms researcher is looking to test out a particular technique that he just devised, he would probably first apply the technique to whatever dataset he was using originally before randomly testing it out on a dataset available on Kaggle.
For what it's worth, there are hundreds of publicly available data sets to test on; if anything, the data available in Kaggle competitions is probably poor relative to the richness in those data sets.
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