Yannis Katsis
Yannis Katsis is a Senior Research Scientist at IBM Research, Almaden with expertise in the management, integration, and extraction of knowledge from structured, semi-structured, and unstructured data. In his recent work, Yannis focuses on lowering the barrier of entry to knowledge extraction by designing, analyzing, and building human-in-the-loop systems that enable domain experts to interactively generate knowledge extraction AI models that serve their needs. Yannis received his PhD in Computer Science from UC San Diego. His work has appeared in top conferences and journals in the areas of data management, natural language processing, and human-computer interaction, and has been leveraged for multiple IBM products as well as open source software.
Sessions
Domain experts often need to create text classification models; however, they may lack ML or coding expertise to do so. In this talk, we show how domain experts can create text classifiers without writing a single line of code through the open-source, no-code Label Sleuth system (www.label-sleuth.org); a system that combines an intuitive labeling UI with active learning techniques and integrated model training functionality. Finally, we describe how the system can also benefit more technical users, such as data scientists, and developers, who can customize it for more advanced usage.