ECE Seminar, Shuchin Aeron

ECE Seminar Shuchin Aeron Tuffs University Department of Electrical and Computer Engineering Faculty Host: Prakash Ishwar Light refreshments will be served outside of PHO 339 at 1:45 pm. Lecture title: Learning with Algebraically Structured Subspaces Abstract: Low dimensional subspace models have had a huge impact on statistical signal processing theory and methods. In this talk we will revisit these subspace models and endow them with algebraic structures that allow group-invariance (submodule) and multilinear (tensor product) properties present in data to be captured in a natural and tractable way. This perspective leads to a number of new classes of subspace models such as the low-rank submodule, the union of submodules, and the union of multilinear subspaces. We will discuss how these new algebraically-structured subspace models can be used to solve a number of multidimensional data processing problems such as data completion, clustering, outlier detection, and denoising in an effective and unified way. Time permitting, we will also highlight some related research directions and recent results. Bio: Shuchin Aeron is an assistant professor in the department of Electrical and Computer Engineering (ECE) at Tufts University. He received his Ph.D. in ECE from Boston University in 2009. From 2009 to 2011 he was a post-doctoral research fellow at Schlumberger-Doll Research (SDR), Cambridge, MA where he worked on signal processing solution products for borehole acoustics. He has several patents in acoustic signal processing and his proposed workflows are currently implemented in SDR’s logging-while-drilling tools. He is the recipient of the 2009 best doctoral dissertation award from both the college of engineering and the ECE department at BU. He received the Center of Information and Systems Engineering (CISE) award from Boston University in 2006 and a Schlumberger-Doll Research grant in 2007. He is currently a senior member of the IEEE. His research interests are in statistical signal processing, information theory, and optimal sampling and recovery.