Tensors and tensor decompositions have been very popular and effective tools for analyzing multi-aspect data in a wide variety of fields, ranging from Psychology to Chemometrics, and from Signal Processing to Data Mining and Machine Learning.

Using tensors in the era of big data poses the challenge of scalability and efficiency. In this talk, I will discuss recent techniques on tackling this challenge by parallelizing and speeding up tensor decompositions, especially for very sparse datasets (such as the ones encountered for example in online social network analysis).

In addition to scalability, I will also touch upon the challenge of unsupervised quality assessment, where in absence of ground truth, we seek to automatically select the decomposition model that captures best the structure in our data.

The talk will conclude with a discussion on future research directions and open problems in tensors for big data analytics.