Matteo Spanio

Everything I have written — 23 papers, 6 projects, 5 posts, 8 news — embedded with intfloat/multilingual-e5-base over 108 text chunks and projected to two dimensions with UMAP. Distance is textual similarity; the lines join each work's closest neighbours; colour is my declared research area, never a clustering of the embedding.

Here: drag to pan, scroll to zoom, click a node for its card — and from a card, trace the shortest chain of similarity links to any other work.

Sound (the speaker) plays the map rather than decorating it. The camera is the listener: pan and zoom move you across and above the constellation, so what you are looking at is what you hear nearby. A signal walks the similarity graph, one note a second, and each research area has its own instrument — tape flutter for the heritage work, a plucked string for the Baroque scores, a reed for musicology. Opening a work sounds it with its nearest neighbours; a traced path plays as a phrase.

Full screen turns the map into a small app: semantic search that runs entirely in your browser, and a slider across 12 alternative projections of the same embedding.

Read the geometry loosely. The areas separate only weakly here — silhouette 0.30 in this picture, 0.15 in the full embedding — because nearly everything is audio, music and machine learning (within-area similarity 0.86, between-area 0.82). The lines are computed in the embedding rather than in the picture, so who connects to whom means more than how far apart things sit.