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?