Rather than focusing on HOW to analyze massive data sets better, Anna’s work, instead, focuses on how to COLLECT more relevant, useful information, how to PROCESS those measurements, and how to EXTRACT information from those observations. In many scientific applications, there are many data we could collect but if we chose wisely or chose a summary of that data, we could answer questions of interest more efficiently. This talk will address how to acquire the data in a different way, in summarized or compressed measurements, knowing that we’re going to extract information from the data later. Anna will focus on several applications including high throughput biological screening and structural health monitoring.
Anna Gilbert received an S.B. degree from the University of Chicago and a
Ph.D. from Princeton University, both in mathematics. In 1997, she was a
postdoctoral fellow at Yale University and AT&T Labs-Research. From 1998 to
2004, she was a member of technical staff at AT&T Labs-Research in Florham
Park, NJ. Since then she has been with the Department of Mathematics at the
University of Michigan, where she is now a Professor. She has received
several awards, including a Sloan Research Fellowship (2006), an NSF CAREER award (2006), the National Academy of Sciences Award for Initiatives in Research (2008), the Association of Computing Machinery (ACM) Douglas Engelbart Best Paper award (2008), the EURASIP Signal Processing Best Paper award (2010), a National Academy of Sciences Kavli Fellow (2012), and the SIAM Ralph E. Kleinman Prize (2013).
Her research interests include analysis, probability, networking, and
algorithms. She is especially interested in randomized algorithms with
applications to harmonic analysis, signal and image processing,
networking, and massive datasets.