[EstudiantesMatemática] Tutorial del Dr. Gonzalo Mateos: "Signal Processing tools for Big Data analysis"

[Paola Bermolen]

Hola,

Les mando un mini-curso muy interesante.
Saludos
Paola


-------- Mensaje reenviado --------
Asunto: 	Tutorial del Dr. Gonzalo Mateos: "Signal Processing tools for 
Big Data analysis"
Fecha: 	Wed, 29 Apr 2015 08:44:25 -0300
De: 	Pablo Musé <pmuse en fing.edu.uy>
Para: 	Federico Larroca <flarroca en fing.edu.uy>, Maria Misa 
<mmisa en fing.edu.uy>, Paola Bermolen <paola en fing.edu.uy>, Matias Di 
Martino <matiasdm en fing.edu.uy>, Martin Sambarino 
<martinsambarino en gmail.com>, Marcelo Fiori <marfiori en gmail.com>
CC: 	Gonzalo Mateos Buckstein <gmateosb en ur.rochester.edu>



Estimados,

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.

*Outline*

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 performance

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.)

*Biographie*

*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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