Amir Joudaki

This study uses sparse autoencoders to analyze hidden representations in single-cell foundation models, revealing interpretable biological and technical features and demonstrating their functional relevance.

The paper introduces a long seed sketch method for sequence-to-graph alignment, improving time-accuracy trade-offs in high mutation rate scenarios.

The paper introduces Tensor Sketch, a method for fast and accurate edit distance estimation in biological sequences, outperforming existing methods in speed and accuracy.

This paper introduces a new compression method for de Bruijn graph labels, showing improved performance over existing methods on real-world datasets.