[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.
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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