About the research
Can computation achieve the advantages of randomized algorithms when high-quality randomness is unavailable?
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Zuckerman's research designs and analyzes randomness extractors and pseudorandom generators to produce high-quality randomness from weak sources. He develops two-source extractor algorithms that combine two defective randomness sources to create robust randomness for algorithms. The group studies conversions of randomized algorithms into deterministic or robust randomized forms, using extractor constructions and pseudorandomness tools. Researchers apply these techniques to complexity theory, coding, network constructions, and hardness-of-approximation questions to understand randomness's role in computation.
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