## Representation learning for tensor data

Tensors play a significant role in modeling and understanding high-dimensional and complex problems in machine learning, signal processing, and many other areas. For example, many applications involve images, videos, or other types of data samples that are naturally structured as tensors. With the prevalence of big data in many applications,
finding low-dimensional representations of data is crucial to keep models reasonably small in order to ensure their efficient and scalable training using large datasets. In this project, I study representation learning methods that exploit the **multimodal structure** of tensor data to efficiently learn **sparse representations** of data. More specifically, I work on developing models that best capture the structure in tensor data as well as designing batch and online algorithms that can deal with big and high-dimensional datasets as well as streaming settings.