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?
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 get too many emails 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?
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 you can see what excites you. For readers interested in my area, I recommend reading about pseudorandomness, say from Salil Vadhan's monograph or my lecture notes, or watch my tutorial on Extractors and Expanders, or read more about expanders from the Hoory-Linial-Wigderson survey or more about randomness extractors from Shaltiel's survey or my survey talk, or take one of my classes; computational complexity, say from Arora-Barak, or take my course; coding theory, say from Guruswami-Rudra-Sudan, or take my class; combinatorics and the probabilistic method, say from 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. From among these choices, read what excites you the most.