David Zuckerman (computer scientist)

David Zuckerman is an American theoretical computer scientist whose work concerns randomness in computation.[1] He is a professor of computer science at the University of Texas at Austin.[2]

David Zuckerman
NationalityAmerican
Alma materUniversity of California at Berkeley
Harvard University
Known forPseudorandomness
AwardsACM Fellow
Simons Investigator
Scientific career
FieldsTheoretical computer science
InstitutionsUniversity of Texas at Austin
ThesisComputing Efficiently Using General Weak Random Sources (1991)
Doctoral advisorUmesh Vazirani

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.[3][4] 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.[5][6]

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.[7][8] 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.[9][10][11] The resulting paper won a best-paper award at the 2016 ACM Symposium on Theory of Computing.[12]

gollark: GTech™ is manipulating the refractive index of the local air.
gollark: Did you know? It was already too late. The bees had approached. GTech™ dominion over reality had begun, and none could escape. One night the bees reached the horizon of the sun, and all the specks began. The swarms of specks, all over the city, and even over the whole town. It would be next year that the bees reached the horizon on the night of December 14, 2011. After a week of resting they came to the end of October. They were too exhausted to continue their journey even upon midnight. In the morning they returned to the city to continue their "trip" that came along with the plague. The evening afternoon after dawn, they crossed to the eastern edge of the city, and began their journey on the night of December 15th. The next day, the bees went on their trip to the west of the city. They went on their journey along the northern coast with an aeroplane. When they arrived in the coast of the east of the city, they had a night sleep, as they had not come along the northern coast any further.
gollark: According to the osmarks.net™ future predictor cuboid™ you are actually.
gollark: Written in Macron, self-bootstrapping, and running literally everything optimally and hypermacronously.
gollark: As opposed to the upcoming MacronOS™.

References
  1. "~diz/RandomSurvey". cs.utexas.edu. Retrieved 2016-09-18.
  2. "David Zuckerman's website".
  3. "David Zuckerman's Curriculum Vitae" (PDF).
  4. "David Zuckerman - The Mathematics Genealogy Project". genealogy.ams.org. Retrieved 2016-09-18.
  5. "ACM Fellows - Award Winners: List By Year". awards.acm.org. Retrieved 2016-09-18.
  6. "Simons Investigators Awardees | Simons Foundation". simonsfoundation.org. Retrieved 2016-09-18.
  7. "David Zuckerman's Publications". cs.utexas.edu. Retrieved 2016-09-18.
  8. "dblp: David Zuckerman". dblp.uni-trier.de. Retrieved 2016-09-18.
  9. "ECCC - TR15-119". eccc.hpi-web.de. Retrieved 2016-09-18.
  10. "New technique produces real randomness | Science News". sciencenews.org. Retrieved 2016-09-18.
  11. "Purifying spoiled randomness with spoiled randomness – Not so Great Ideas in Theoretical Computer Science". mittheory.wordpress.com. Retrieved 2016-09-18.
  12. "Computational Complexity: STOC 2016". blog.computationalcomplexity.org. Retrieved 2016-09-18.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.