Applications of Sparse Signal Representations: From Signal Processing to Finance
|Event Date:||February 23, 2011|
|Speaker Affiliation:||DRW Trading Group, Chicago|
|Sponsor:||Communications, Networking, Signal & Image Processing|
|Contact Name:||Prof. Ilya Pollak
|Contact Phone:||(765) 49-45916
|Open To:||ACCEPTABLE FOR ECE 694A
After a brief review of sparse signal representation, we consider its practical applications: first we will look at source localization in sensor arrays and describe a method that produces highly sparse spatial spectra via second order cone programming. Next we will overview several applications in quantitative finance: construction of sparse Markowitz portfolios, structured risk modeling, and sparsity in statistical arbitrage. Time permitting we will also touch upon the role of sparsity in designing robust estimators.
Dmitry Malioutov is a researcher in the Algorithmic Trading Group at DRW, Chicago. He obtained his Ph.D. from MIT EECS in 2008, where his research focused on message-passing schemes for inference in graphical models, and sparse signal representation. Prior to joining DRW Dmitry held a postdoctoral position at Microsoft Research, Cambridge, UK. His current interests include statistical risk modeling, structured high-dimensional statistical inference, and convex optimization.