Graduating students in Statistics appear to be at a substantial handicap compared to graduating students in Machine Learning, despite being in substantially overlapping subjects.<\/p>\n
The problem seems to be cultural. Statistics comes from a mathematics background which emphasizes large publications slowly published under review at journals. Machine Learning comes from a Computer Science background which emphasizes quick publishing at reviewed conferences. This has a number of implications:<\/p>\n
In short, Stats students have had relatively few chances to distinguish themselves and are heavily reliant on their advisors for jobs afterwards. This is a poor situation, because advisors have a strong incentive to place students well, implying that recommendation letters must always be considered with a grain of salt.<\/p>\n
This problem is more or less prevalent depending on which Stats department students go to. In some places the difference is substantial, and in other places not.<\/p>\n
One practical implication of this, is that when considering graduating stats PhDs for hire, some amount of affirmative action is in order. At a minimum, this implies spending extra time getting to know the candidate and what the candidate can do is in order. <\/p>\n","protected":false},"excerpt":{"rendered":"
Graduating students in Statistics appear to be at a substantial handicap compared to graduating students in Machine Learning, despite being in substantially overlapping subjects. The problem seems to be cultural. Statistics comes from a mathematics background which emphasizes large publications slowly published under review at journals. Machine Learning comes from a Computer Science background which … <\/p>\n