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November 10, Tuesday
12:00 – 14:00

In numerous areas of science and engineering, we seek to extract information from linearly derived measurements in a computationally feasible manner. Linear measurement systems include medical imaging, financial prediction, seismic imaging in the oil industry, multiuser communication among cellphones, etc. Much of my discussion will focus on compressed sensing, an emerging area based on the revelation that optimization routines can reconstruct a sparse signal from a small number of linear projections of the signal - but the results are far more general. I will describe recent investigations into the ultimate performance limits that can be achieved in linear measurement systems, and describe an algorithm that achieves asymptotically optimal performance when the system is large and the measurement process can be modeled by a sparse matrix. An overview of potential future directions will be provided.