This last semester, I was a long-term visitor at the Simons Institute for the Theory of Computing. My time there was rather productive, resulting in a few (exciting!) arXiv preprints, which I discuss below.

**1. SqueezeFit: Label-aware dimensionality reduction by semidefinite programming.**

Suppose you have a bunch of points in high-dimensional Euclidean space, some labeled “cat” and others labeled “dog,” say. Can you find a low-rank projection such that after projection, cats and dogs remain separated? If you can implement such a projection as a sensor, then that sensor collects enough information to classify cats versus dogs. This is the main idea behind compressive classification.

Continue reading A few paper announcements