The rapid advancement and proliferation of information and communication technologies (ICT) in the past two decades significantly impacted how we do research. For instance, our research increasingly relies on growing computing power and storage capacity to gather and process the constant production of data - our observations of systems and phenomena we would like to understand, control, and improve.

Webinar Details: Wednesday, 13 December 2017 from 2:30pm - 3:30pm (New York time) 
Attendee Registration Link: Register for Webinar, by Dr. Nikos Sidiropoulos

CAMSAP 2017 Plenary - Tensors and Probability: An Intriguing Union: Abstract: We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. 

Spy satellites and their commercial cousins orbit Earth like a swarm of space paparazzi, capturing tens of terabytes of images every day. The deluge of satellite imagery leaves U.S. intelligence agencies with the world’s biggest case of FOMO—“fear of missing out”—because human analysts can sift through only so many images to spot a new nuclear enrichment facility or missiles being trucked to different locations.

Neural networks are a set of biologically inspired algorithms that can be used to recognize patterns. Deep neural networks (DNNs) are neural networks that have much more layers in depth than traditional neural networks.

In the past decade, there has been a transformative and permanent revolution in computer vision cultivated by the reinvigorated adoption of deep learning for visual understanding tasks.