#2 — Deanna Needell

About this episode

Deanna Needell discusses her research in mathematics and its applications in various fields.

Deanna Needell is a Professor of Mathematics at University of California, Los Angeles (UCLA) and a leading researcher in compressed sensing, numerical linear algebra, data science, and machine learning. Her work has shaped modern sparse recovery and randomized iterative algorithms, and she is widely known for co-developing CoSaMP, a cornerstone method in compressed sensing. More broadly, her research connects linear algebra and optimization with machine learning. Deanna’s research excellence has been recognized with several honors, including the IMA Prize in Mathematics and its Applications, an NSF CAREER Award, a Sloan Research Fellowship, and election as a Fellow of the American Mathematical Society and a Fellow of SIAM. Beyond theory, Deanna has applied mathematical tools to real-world problems in areas such as imaging, public health, and legal analytics, including work on Lyme disease data and collaborations with organizations like the California Innocence Project. She serves as the Executive Director for the Institute for Digital Research and Education and the Dunn Family Endowed Chair in Data Theory, and is deeply committed to mentorship, inclusiveness, and building bridges…

People in this episode

Guest: Deanna Needell

Topics covered

• compressed sensing
• numerical linear algebra
• data science
• machine learning
• sparse recovery
• optimization

Keywords

• compressed sensing
• numerical linear algebra
• data science
• machine learning
• sparse recovery
• optimization
• mentorship
• public health

Mentioned in this episode

Organizations: University of California, Los Angeles, California Innocence Project, American Mathematical Society, SIAM