FAQ for Prospective Students and Interns

UT Austin is an excellent place for research in theoretical computer science, and I like to work with top students. I therefore receive many emails asking the following.

• Are you accepting new Ph.D. students?

Unfortunately, I'm not accepting new students at the moment. (Last updated: September 2023.)


• Should I apply to the Ph.D. program at UT Austin? What are my chances of being accepted?

Admission to our Ph.D. program is decided by a committee. Emailing individual professors doesn't help. The decision is based on a variety of criteria; see our graduate Admissions FAQ. I don't have time to evaluate your chances based on your CV, especially because reference letters play a key role. If you're interested in working with me, do mention this in your application. Once you're admitted, I'm happy to discuss anything with you at length.


• Do you have any internships for undergraduates?

Unfortunately, no.


• I really want to work with you. What should I read?

For an introduction to my area, listen to my 100-second talk about randomness on the Academic Minute, or read my two essays for a general audience.

At the undergraduate level, read about computational complexity, say from Mike Sipser's book (or take my course); algorithms, say from Kleinberg-Tardos (or take my class); probability and randomized algorithms, say from Mitzenmacher-Upfal (or take my course); and supporting math classes, including probability, linear algebra, algebra, and number theory.

At the graduate level, Avi Wigderson's book gives an excellent overview of theoretical computer science, and see what excites you. For pseudorandomness, watch videos from the Simons Pseudorandomness Boot Camp, or read Salil Vadhan's monograph or my lecture notes, or take one of my classes. For computational complexity, read Arora-Barak, or take my course; for coding theory, read Guruswami-Rudra-Sudan, or take my class; for combinatorics and the probabilistic method, read Alon-Spencer, Jukna, or van Lint-Wilson, or take my course; randomized algorithms, say from Motwani-Raghavan, or take my class; analysis of Boolean functions, say from O'Donnell's book; and probability, say Roman Vershynin's High Dimensional Probability.