[inciampati]

This is a major practical advance from the succinct data structure community.

This community has produced so many brilliant results in the past years. But, they work in the shadows. Since the rise of interest in neural network methods, I've often described their work as "machine learning where epsilon goes to 0." It's not sexy, but it is extremely useful.

For instance, Ferragina previously helped to develop the FM-index that enabled the sequence alignment algorithms used for the primary analysis of short genomic reads (100-250bp). These tools were simply transformative, because they reduced the amount of memory required to write genome mappers by orders of magnitude, allowing the construction of full-text indexes of the genome on what was then (~2009) commodity hardware.