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

[Paola Bermolen]

----- Mensaje reenviado de Pablo Musé <pmuse en fing.edu.uy> -----
  Fecha: Thu, 30 Apr 2015 16:48:08 -0300
     De: Pablo Musé <pmuse en fing.edu.uy>
Asunto: Tutorial del Dr. Gonzalo Mateos: "Signal Processing tools for  
Big Data analysis"
   Para: Federico Larroca - IIE <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>,  
"docentes_iie en fing.edu.uy laura en fing.edu.uy"  
<docentes_iie en fing.edu.uy>, Laura Landin <lalandin en fing.edu.uy>
     Cc: Gonzalo Mateos Buckstein <gmateosb en ur.rochester.edu>

Estimados, perdón por el error. El tutorial dura 3 horas en total, no  
sé de dónde saqué lo de los tres días. Así que va a ser el miércoles  
13/5, de 12 a 15h30. El otro cambio es el salón; por la cantidad de  
gente que manifestó su interés, el salón de seminarios de electro va a  
quedar chico, así que estamos tramitando la reserva de otro salón. Los  
tendré informados en breve. Les pido rectifiquen la info a quienes se  
la hayan reenviado.

Disculpas por el ruido.

Saludos,

Pablo

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 de 12h a 15h30 (pausa de 30 minutos en el medio).

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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