Randomized Numerical Linear Algebra (RNLA) focuses on developing efficient and practical algorithms for solving problems using numerical linear algebra via randomized techniques. Such methods have gained significant interest in the last 20 years due to the availability of large-scale data, and the challenges that arise when working with it. While computational capabilities have witnessed substantial growth, memory latency constraints persist. This one-week workshop aims to bring together researchers working in RNLA and closely related areas to discuss and initiate collaborations on open problems. The workshop will focus on three subtopics: (i) randomization in algorithms, (ii) randomized multilinear algebra, and (iii) applications to machine learning and inverse problems.
This week-long workshop assembles researchers at diverse career stages with the shared objective of engaging in in-depth discussions and hands-on problem-solving within RNLA. Our primary aim is cultivating new, long-lasting collaborations among mathematicians and researchers in this specialized field. Participants typically range from graduate students to senior faculty members and professional researchers in government and industry labs. We intend to leverage this one-week workshop as an inaugural platform, fostering a conducive environment for participants to convene in person to collectively explore problems of mutual interest, exchange expertise, and establish meaningful connections that transcend the workshop, paving the way for sustained collaboration and innovation.
Malena Español
(Arizona State University)
Jamie Haddock
(Harvey Mudd College)
Anna Ma
(University of California, Irvine (UCI))
Deanna Needell
(University of California, Los Angeles (UCLA))
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