<p>I'm a first year PhD student in a CS department at what some would probably consider a mid-tier university. I've been reading from Vershynin's High-Dimensional Probability and been trying to learn about concentration; I like problems relating to probability and geometry (e.g compressed sensing, dimension reduction, matrix recovery, etc.) But I'm wondering, more broadly: </p>

<ul>
<li>If you could go back to grad school, what would you be doing during your first couple of years to learn techniques, etc.? </li>
<li>One thing I struggle with is determining which papers to read. There are so many to pick from; is there a better strategy than just reading whatever seems to pique interest/come from authors you know? </li>
<li>Are there books/problems/papers you read during grad school that helped you either technically or in forming your research interests/problem taste? </li>
</ul>

<p>Thanks!</p>

CC BY-SA 4.0
2019-10-30T10:59:46.883Z
Terrence Tao has a bunch of advice on his blog for all stages of a math carreer, you might find some of it useful: https://terrytao.wordpress.com/career-advice/
Denis
CC BY-SA 4.0
2019-10-31T03:45:08.277Z
I work in machine learning, and I never got a great foundation in high dimensional probability.  I worked through Vershynin's book as a postdoc and wished I had found a resource like that much earlier.  It's a great book, and you definitely can't go wrong spending time working problems in the book.  If I had a time machine, I would go back and spend more time on high quality textbooks like his and less time on reading random papers.
Mike Izbicki
<soft-question>
Advice for new grad student
Drew Brady
# Advice for new grad student

(Asked by: username_0 on 2019-10-30)

I'm a first year PhD student in a CS department at what some would probably consider a mid-tier university. I've been reading from Vershynin's High-Dimensional Probability and been trying to learn about concentration; I like problems relating to probability and geometry (e.g compressed sensing, dimension reduction, matrix recovery, etc.) But I'm wondering, more broadly:

* If you could go back to grad school, what would you be doing during your first couple of years to learn techniques, etc.?
* One thing I struggle with is determining which papers to read. There are so many to pick from; is there a better strategy than just reading whatever seems to pique interest/come from authors you know?
* Are there books/problems/papers you read during grad school that helped you either technically or in forming your research interests/problem taste?

Thanks!

**Tags:** soft-question

> **Comments:**
> **username_1**: Terrence Tao has a bunch of advice on his blog for all stages of a math carreer, you might find some of it useful: https://terrytao.wordpress.com/career-advice/  
> **username_2**: I work in machine learning, and I never got a great foundation in high dimensional probability.  I worked through Vershynin's book as a postdoc and wished I had found a resource like that much earlier.  It's a great book, and you definitely can't go wrong spending time working problems in the book.  If I had a time machine, I would go back and spend more time on high quality textbooks like his and less time on reading random papers.  

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## 0 answers

