Research Overview

As industries across the globe pursue digitization across functions, more and more data are being generated and utilized to empower decision making and insight generation. As the volume and complexity of data increases, it is also becoming difficult for traditional machine learning (ML) algorithms to make sense of a large number of variables. The labeling and annotation of such large and complex datasets are highly laborious and time consuming, making ML unscalable.

While most of the current ML-based systems depend largely on supervised ML algorithms, unsupervised learning (UL) systems after years of theoretical and lab research have found applicability in commercial applications and have been at the center of many initiatives in industries such as automotive, finance, and cybersecurity.