Associate Professor Cai Kui

Project Info

The amount of digital data produced by humanity will be kept growing exponentially “Big”. The needs of increasing the information density of data storage systems have become even more pressing with the surge of Big Data and AI. Most of the world’s data today is stored on magnetic and optical recording media. Due to the physical limitation of these technologies, the storage of zettabytes of data would still occupy large physical space, with short durability, and high maintenance cost. The DNA-based data storage technology, which stores digital data using synthetic DNA, has emerged as a very promising candidate for the storage of Big Data, due to its extremely high storage density, long lasting stability of hundreds to a thousand year, and ultra-low power consumption for operation and maintenance.

The objective of the Ph.D’s work is to design and analyze novel and efficient information coding and error correction algorithms and techniques to enable reliable and practical DNA-based data storage systems. The Ph.D work will focus on the design of novel and efficient channel codes for DNA-based data storage systems. Both error correction codes (ECCs) and constrained codes will be especially designed and optimized for the DNA-based Data Storage channels.
The Ph.D work is designed to address the major technical challenges of the data storage industry, and hence the students will find plenty opportunities from the data storage industry, the biotech industry, and the academia once graduated. The corresponding companies include (but not limited to) Broadcom, Marvell, Western Digital (WD), Seagate, Hwawei, Toshiba, etc.