Zhiying Wang
University of California-Irvine, CA·Engineering·Early-career
DNA can pack enormous amounts of data into a tiny, long-lasting molecule, which makes it attractive for storing digital information. Writing data into DNA is expensive, though.
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
DNA data storage
composite DNA
coding theory
- Grants
- 1 known grant, through Jan 2028
What they work on
- 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.
- Develop information-theoretic security schemes for composite DNA storage, including methods where secret data can be decoded only by reading mixed strands from multiple storage vessels.
- Devise methods to identify a target composite DNA strand within a vessel holding many strands, and verify the coding schemes through software simulation and experiments.
More about the research
Synthesizing DNA costs about $0.10 per nucleotide position. Because standard storage uses one of the four bases (A, C, G or T) per position, that works out to at least $0.05 per bit. Composite DNA storage offers a cheaper route. Each position holds a mixture of all four bases across many copies of a strand, in set proportions. This raises information density and could cut cost per bit by several orders of magnitude.
Why it matters
Cheap, dense DNA storage could help with the growing need for long-term data storage. By lowering the cost per bit of writing data into DNA and adding reliability and security guarantees, this coding work could move DNA storage closer to wide use in a broad range of applications.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Dana Dachman-Soled
University of Maryland, MD·Computer Science
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.
Hessam Mahdavifar
Northeastern University, MA·Engineering·Early-career
Generalize Plotkin Transform codes, the family containing Reed–Muller and polar codes, through nonlinear parameterizations, and search for new codes using machine learning, deep learning and gradient-descent optimization.
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.
The school
University of California-Irvine: 46 labs on PI Pages, $645M a year on research, 524 research doctorates a year.
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