Nationality American Known for Pseudorandomness | Doctoral advisor Umesh Vazirani Academic advisor Umesh Vazirani | |
Fields Theoretical computer science Institutions University of Texas at Austin Thesis Computing Efficiently Using General Weak Random Sources (1991) Doctoral students Abhishek Bhowmick
Eshan Chattopadhyay
Xin Li
Raghu Meka
Anup Rao Alma maters University of California, Berkeley, Harvard University Institution University of Texas at Austin | ||
David Zuckerman is an American theoretical computer scientist whose work concerns randomness in computation. He is a professor of computer science at the University of Texas at Austin.
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Biography
Zuckerman received an A.B. in mathematics from Harvard University in 1987, where he was a Putnam Fellow. He went on to earn a Ph.D. in computer science from the University of California at Berkeley in 1991 advised by Umesh Vazirani. He then worked as a postdoctoral fellow at the Massachusetts Institute of Technology and Hebrew University of Jerusalem before joining the University of Texas in 1994. Zuckerman was named a Fellow of the ACM in 2013, and a Simons Investigator in 2016.
Research
Most of Zuckerman's work concerns randomness in computation, and especially pseudorandomness. He has written over 80 papers on topics including randomness extractors, pseudorandom generators, coding theory, and cryptography. Zuckerman is best known for his work on randomness extractors. In 2015 Zuckerman and his student Eshan Chattopadhyay solved an important open problem in the area by giving the first explicit construction of two-source extractors. The resulting paper won a best-paper award at the 2016 ACM Symposium on Theory of Computing.