אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In the last decade, DNA-based storage systems emerged as a potential data archival solution due to their high data density and durability. This research delves into intrinsic error characteristics of DNA storage systems to devise robust coding strategies and innovative algorithms for enhanced reliability, efficiency, scalability, and cost-effectiveness. The research propels DNA storage feasibility while contributing to foundational theory.

The work analyzes combinatorial structures tied to errors that are common in DNA storage, like insertions and deletions, alongside examining corresponding channels. These explorations illuminate DNA storage's unique error behavior, offering insights into its fundamental limits and capabilities.

Moreover, to advance DNA storage-related coding techniques, this work introduces novel code constructions spanning from innovative reconstruction codes to streamlined constraint coding algorithms. These efforts underscore coding techniques' pivotal role in DNA storage advancement.

Bridging theory with application, the DNA coverage depth problem, a delicate balance involving sequencing costs, latency, and retrieval accuracy, is introduced and addressed in this work. A pioneering proof-of-concept for a scalable DNA storage pipeline that integrates Deep Neural Networks with coding strategies is introduced. It enables error-free retrieval with state-of-the-art accuracy and improvement of orders of magnitude in efficiency.