 Prof.
Martin J. Strauss,
assistant professor of Computer Science and Engineering, and of
Mathematics, was recently awarded an NSF Faculty Early Career Development
(CAREER) award for his research project, “Next-Generation Algorithmics for
Sparse Recovery.”
This project is concerned with massive datasets, and the ability to
retrieve useful information from them through new algorithms that are
exponentially faster than classical algorithms. Massive datasets abound in a
wide variety of applications facing academic researchers, government
agencies, and corporations, such as: experiments with particle colliders;
telecommunications, including high-frequency RADAR as well as voice and data
networks; and transactional data arising from commerce, medical care, and
other areas.
Prof. Strauss acknowledges that he is not proposing a solution to the
most complex of these problems, such as RADAR. Rather, he makes the case
that massive dataset issues will arise in RADAR and that RADAR engineers
need to adjust their thinking to incorporate massive dataset algorithmics—the
sooner the better.
The project will involve collaboration with specialists in other areas
such as medical imaging and RF signal processing. “The techniques being
developed will change the way we think about massive dataset algorithmics,”
stated Prof. Strauss.
Techniques developed by Prof. Strauss are relevant to a new degree
program in Informatics being offered at the University of Michigan. He also
intends to work them into a special “Super Science Show” for the Ann Arbor
Hands-on Museum.
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Martin Strauss is a member of the Theory of Computation group in the
division of Computer Science and
Engineering. His research interests include fundamental algorithms,
especially randomized and approximation algorithms; algorithms for massive
data sets; signal processing and computational harmonic analysis; computer
security and cryptography; and complexity theory.
The CAREER award is one of NSF’s most prestigious awards, conferred for
“the early career-development activities of those teacher-scholars who most
effectively integrate research and education within the context of the
mission of their organization.”
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