[Todos CMAT] curso "Signal processing for big data analysis"

mordecki en cmat.edu.uy mordecki en cmat.edu.uy
Mie Abr 29 10:58:42 UYT 2015

Ruego den mayor difusión al evento siguiente (docentes, estudiantes 
avanzados de grado y de posgrado de ingeniería eléctrica, física, 
ingeniería matemática y matemática):

Tutorial "Signal Processing tools for Big Data analysis"a cargo del Dr. 
Gonzalo Mateos, Universidad de Rochester NY.

Horario: miércoles 13, jueves 14 y viernes 15 de 12h a 15h30 (pausa de 30 
minutos en el medio). Lugar: Salón de Seminaros del Instituto de Ingeniería 
Eléctrica, Facultad de Ingeniería.

Summary We live in an era of data deluge. Pervasive sensors collect massive 
amounts of information on every bit of our lives, churning out enormous 
streams of raw data in various formats. Mining information from 
unprecedented volumes of data promises to limit the spread of epidemics and 
diseases, identify trends in financial markets, learn the dynamics of 
emergent social-computational systems and also protect critical 
infrastructure including the smart grid and the Internet's backbone 
network. While Big Data can be definitely perceived as a big blessing, big 
challenges also arise with large-scale datasets. The sheer volume of data 
makes it often impossible to run analytics using a central processor and 
storage, and distributed processing with parallelized multi-processors is 
preferred while the data themselves are stored in the cloud. As many 
sources continuously generate data in real time, analytics must often be 
performed "on-the-fly" and without an opportunity to revisit past entries. 
Due to their disparate origins, the resultant datasets are often incomplete 
and include a sizable portion of missing entries. In addition, massive 
datasets are noisy, prone to outliers and vulnerable to cyber-attacks. 
These effects are amplified if the acquisition and transportation cost per 
datum is driven to a minimum. Overall, Big Data present challenges in which 
resources such as time, space and energy are intertwined in complex ways 
with data resources. Given these challenges, ample signal processing (SP) 
opportunities arise. This tutorial seeks to provide an overview of ongoing 
research in novel models applicable to a wide range of Big Data analytics 
problems, as well as algorithms and architectures to handle the practical 
challenges, while revealing fundamental limits and insights on the 
mathematical trade-offs involved.


I. Introduction, motivation and context (20 mins.)
II. Theoretical and statistical foundations for Big Data Analytics (1 hr.)

a) High-dimensional statistical SP and succinct data representations;

i) Compressive sampling, sparsity, and (non-linear) dimensionality 
reduction ii) Low-rank models, matrix completion, and regularization for 
underdetermined problems

b) Robust approaches to coping with outliers and missing data;
c) Big tensor data models and factorizations; streaming analytics

III. Algorithmic advances for mining massive datasets (45 mins.)

a) Scalable, online, and decentralized learning and optimization; b) 
Randomized algorithms for very large matrix, graph, and regression 
problems; c) Convergence analysis, computational complexity, and 

IV. Random sampling and consensus ideas for Big Data Analytics (45 mins.)

a) Sketching for (non) parametric regression and dynamic data tracking;
b) Classification and clustering large-scale, high-dimensional datasets;

V. Concluding remarks (10 mins.)


Gonzalo Mateos was born in Montevideo, Uruguay, in 1982. He received his 
B.Sc. degree in Electrical Engineering from Universidad de la Republica, 
Uruguay, in 2005 and the M.Sc. and Ph.D. degrees in Electrical Engineering 
from the University of Minnesota (UofM), Twin Cities, in 2009 and 2011. 
>From 2004 to 2006, he worked as a Systems Engineer at Asea Brown Boveri 
(ABB), Uruguay. During the 2013 academic year, he was a visiting scholar 
with the Computer Science Dept., Carnegie Mellon University. Since 2014, he 
has been an Assistant Professor with the Department of Electrical and 
Computer Engineering at the University of Rochester, Rochester, NY. His 
research interests lie in the areas of statistical learning from Big Data, 
network science, wireless communications and signal processing. His current 
research focuses on algorithms, analysis and application of statistical 
signal processing tools to dynamic network health monitoring, social, power 
grid and Big Data analytics. Since 2012, he serves on the Editorial Board 
of the EURASIP Journal on Advances in Signal Processing. He received the 
Best Student Paper Award at the 13th IEEE Workshop on Signal Processing 
Advances in Wireless Communications, 2012, held at Cesme, Turkey, and was 
also a finalist of the Student Paper Contest at the 14th IEEE DSP Workshop, 
2011, held at Sedona, Arizona, USA. His doctoral work has been recognized 
with the 2013 UofM's Best Dissertation Award (Honorable Mention) across all 
Physical Sciences and Engineering areas.

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