r/statistics • u/GayTwink-69 • 10d ago

Career Mathematical Statistics VS Computational Statistics/Machine Learning for an academic careeer [C] [R}

Is it still worth pursuing an academic career in mathematical statistics these days?
Or is it shooting yourself in the foot with all the focus on computational statistics and machine learning?

I.e., will you have a harder time landing postdocs/tenure track positions and getting grants as a more mathematical statistician vs a computational statistician/machine learning scientist?

I love mathematical statistics and proving everything rigorously using mathematics, but I also don't want to be shooting myself in the foot by choosing a dead/dying path.

37 Upvotes

90% Upvoted

11 comments sorted by

View all comments

-10

u/Alternative_Lack9983 10d ago edited 10d ago

It depends on the type of mathematical statistics you are going to work on. In my opinion, a lot of it has become quite irrelevant (e.g. high-dimensional statistics LASSO-type, GMM type work, Bayesian asymptotics and non-parametrics, functional data analysis etc.). However, there is a lot of interesting new statistics arising from deep learning and modern ML (see e.g., the work of Peter Bartlett and Emmanuel Candes) which do require maths. If you don't plan to stay in academia, go for Comp stats/ML without any hesitation. Industry does not care about theory.

2

u/empiricalprocessor 10d ago

Can you provide other names beyond Bartlett and Candes of people who work on theoretical topics that you deem interesting?

5

u/Alternative_Lack9983 9d ago

Some more junior people: Angelopoulos in Stanford, Ramdas in CMU, Dobiran in Wharton, Niles-Weed in NYU -- all doing highly relevant work, neat combination of methodology and theory.