Title: Simple Classification using Binary Data
Speaker: Deanna Needell (University of California Los Angeles)
Abstract: Joint work with Rayan Saab and Tina Woolf.
Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this talk, we provide a brief background to sparsity and 1-bit measurements, and then present new results on the problem of data classification from binary data that proposes a framework with low computation and resource costs. We illustrate the utility of the proposed approach through stylized and realistic numerical experiments, provide a theoretical analysis for a simple case, and discuss future directions.
Bio: Deanna Needell earned her PhD from UC Davis before working as a postdoctoral fellow at Stanford University. She is currently a full professor of mathematics at UCLA. She has earned many awards including the IEEE Best Young Author award, the Hottest paper in Applied and Computational Harmonic Analysis award, the Alfred P. Sloan fellowship, an NSF CAREER and NSF BIGDATA award, and is the current recipient of the IMA prize. She is currently participating as a research professor fellow at MSRI this Fall. She also serves as associate editor for IEEE Signal Processing Letters, Linear Algebra and its Applications, and Transactions in Mathematics and its Applications as well as on the organizing committee for SIAM sessions and the Association for Women in Mathematics.
Colloquium URL: https://web.math.osu.edu/colloquium/