Questo : Interactive Objective Function as a tool for Model Selection

Faculty: 
Alex Endert
Students: 
Subhajit Das

Visual analytics (VA) systems with semantic interaction help users craft machine learning (ML) based solutions in various domains such as bio-informatics, finance, sports, etc.
However current semantic interaction based approaches are data and task-specific which might not generalize across different problem scenarios. In this project, we describe a novel technique of abstracting user intents and goals in the form of an interactive objective function which can guide any auto-ML based model optimizer (such as Hyperopt, Sigopt, etc.) to construct classification models catering to the expectations of the user. The objective function enables the auto-ML model optimizer to find the best classification models based on criteria's specified by the user. We believe abstraction of user intents in a mathematical form facilitates the generation of personalized ML solutions for any dataset, task or problem scenario.  

Lab: 
Director: 
Alex Endert
Faculty: 
Alex Endert
Our goal is to help people make sense of data. We research and develop interactive visualizations that couple machine learning with visual interfaces of data for exploration and sensemaking.