Applied Harmonic Analysis, Massive Data Sets, Machine Learning, and Signal Processing (16w5136)

Arriving in Oaxaca, Mexico Sunday, October 16 and departing Friday October 21, 2016


(University of California, Davis)

Emmanuel Candes (Stanford University)

Ronald Coifman (Yale University)

(Princeton University)


The Casa Matemática Oaxaca (CMO) will host the "Applied Harmonic Analysis, Massive Data Sets, Machine Learning, and Signal Processing" workshop from October 16th to October 21st, 2016.

Advances in technology and the ever-growing role of digital sensors and computers in science have led to an exponential growth
in the amount and complexity of data we collect.
Uncertainty, scale, non-stationarity, noise, and heterogeneity are fundamental issues
impeding progress at all phases of the pipeline that creates knowledge from data.
Thus there is an pressing demand for new mathematical tools for analyzing massive and complex data sets.
The potential benefits for science and society are manifold, ranging from new scientific discoveries and better tools for medical diagnostics
to more accurate predictions of long-term environmental trends and more efficient technical devices.

The workshop will bring together world leading experts at the intersection of
applied harmonic analysis, machine learning, optimization,
and signal processing to present recent developments and to foster new interactions. Applied harmonic analysis
builds novel architectures for information. Machine learning extracts features from large data sets. Signal processing
is concerned with the denoising, recovery, and transformation of data.
Optimization provides powerful numerical algorithms utilized by the other three areas. Until now,
these four areas have interacted only in ad hoc ways. The direct interaction of mathematicians, statisticians, engineers, and computer scientists,
made possible by this workshop, will make for an efficient intellectual feedback loop, which is central to achieving the urgently needed breakthroughs
in the area of ``Big Data''.

The Casa Matemática Oaxaca (CMO) in Mexico, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, are collaborative Canada-US-Mexico ventures that provide an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry.

The research station in Banff is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT). The research station in Oaxaca is funded by CONACYT.