Dana Dachman-Soled
University of Maryland, MD·Computer Science
Computers rely on two resources most people take for granted: good random bits and reliable ways to send and store information. Randomness underpins encryption, privacy and randomized algorithms.
- Fields
Computer Science
Mathematics & Statistics
- Topics
theoretical computer science
cryptography
complexity theory
- Grants
- 1 known grant, through Jul 2029
What they work on
- Construct deterministic randomness extractors, which turn imperfect random sources into high-quality random bits, for efficiently samplable weak sources, including low-entropy classical and quantum sources and sources subject to leakage.
- Build non-malleable extractors whose outputs stay random even when an adversary sees results from tampered or correlated versions of the same source, with emphasis on adaptive tampering and many correlated sources.
- Develop error-correcting and non-malleable codes for computationally bounded channels, including best-of-both-worlds codes that recover messages under limited errors and otherwise reveal nothing meaningful about them.
- Use complexity-theoretic hardness, derandomization and coding-theoretic reductions, treating quantum polynomial time as a model of physically realizable processes, to prove guarantees beyond information-theoretic limits.
More about the research
Error-correcting codes let data survive noise, failures and tampering. Classical information theory assumes that random sources, channels and attackers can behave in arbitrary worst-case ways, and under that assumption many useful guarantees are impossible. This project asks what becomes possible when those processes are limited to what can actually be computed.
Why it matters
Reliable randomness and robust communication underlie secure messaging, data storage and privacy-preserving systems. By showing how to get strong guarantees against realistic, computationally limited sources and attackers, this work may strengthen the theoretical foundations of secure and resilient information systems and trustworthy digital infrastructure.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Paul Valiant
Purdue University, IN·Computer Science
Design error-correcting codes that withstand random or adversarial insertion and deletion errors while supporting local decoding, which recovers part of a message by reading only a small piece of the stored data.
Zhiying Wang
University of California-Irvine, CA·Engineering·Early-career
Establish the fundamental limit on information density for composite DNA storage, where each position is a set mixture of A, C, G and T, and design coding schemes that reach it given symbol choice and storage-channel errors.
Ryan O'Donnell
Carnegie Mellon University, PA·Computer Science
Find new elementary methods for constructing high-dimensional expanders, highly interconnected mathematical networks, that avoid the advanced algebra and number theory used in current constructions.
The school
University of Maryland: 49 labs on PI Pages, $1.5B a year on research, 602 research doctorates a year.
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