NSSIM 2013 - 2013 Symposium on: New Sensing and Statistical Inference Methods

We are living in interesting times. Our societies have begun to embrace the transition to a digital world in which computation and data will fuel economic developments, social changes, scientific discoveries, and technological innovations. Indeed, businesses, governments, scientists, engineers, medical professionals, etc., are increasingly relying on computation- and data-enabled methods for achieving their goals. Still, a complete transition to a world in which computation and data facilitate decision making for social and economic uplifting, and improve overall life quality requires meeting huge challenges. The Symposium on New Sensing and Statistical Inference Methods is meant to explore solutions to two of these challenges, namely, sensing and statistical inference. Both these challenges are rather grand in nature and successfully addressing them requires a concerted effort by practitioners and theoreticians working at the intersection of signal processing, statistics, harmonic analysis, machine learning and systems engineering. The motivation for this symposium arises from the need to provide a common platform for the exchange of ideas among a diverse group of researchers, with a common focus on sensing and statistical inference problems of the future.
Submissions of at most 4 pages in two-column IEEE format are welcome on topics including:
Active learning and adaptive sampling
Compressive-sensing-inspired systems
Computational imaging systems
Computational methods for "big data"
Data-adaptive representation theory/Dictionary learning
Distributed statistics/machine learning
High-dimensional statistical inference
Manifold-based signal processing
New sensing paradigms in medical imaging
Information processing in social networks
Robust statistical inference
Sensing/inference for biological processes
Sensing/processing of hyperspectral data
Statistical inference in graphical models
Keynote Speakers
Eric Kolaczyk, Boston University
Pierre Vandergheynst, EPFL
Alfred Hero, University of Michigan