Host Faculty: Professor Ivan Selesnick
I will talk about the adoption of some combinatorial techniques in the reconstruction of high dimensional sparse data sets and low rank matrices from lower dimensional linear projections. The proposed techniques facilitate the decoding of (analog) sparse signals with extremely large dimensions in times substantially less time than the traditional convex optimization and greedy based approaches. I will mention specific statistical inference applications where the proposed methods can be used for dimensionality reduction and learning. Examples of such application are in neighbour discovery in wireless ad-hoc networks, market basket analysis, prediction of financial market events, political ranking and digital health.
Amin Khajehnejad finished his PhD in electrical engineering from California Institute of Technology in June 2012. His areas of expertise are statistical signal processing, optimization, machine learning, dimensionality reduction techniques, information theory and coding. His thesis was titled "combinatorial regression and improved basis pursuit for sparse recovery", for which he received the Wiltz prize for outstanding research in electrical engineering. He obtained MS and BS degrees in electrical engineering from Caltech and University of Tehran in 2009 and 2007, respectively. In addition, he has had consulting appointments with various firms and research labs such as Lyric Semiconductor Inc., NEC Laboratories America, Proteus Digital Health, D.E Shaw & Co. and some other financial firms.