Computer Human Accessability and Interaction (CHAI Lab)

Our Goal

Our goal is to push the envelope in human-computer interaction (HCI) techniques, research methods, and tools to make computer interfaces more functional and enable greater engagement from its users

CHAI Lab research focuses on user experience and user performance optimization via measurement of the user’s cognitive state. I do this by leveraging tools such as psychophysical measurement, machine learning, neural networks, and statistical analysis. There are two problems that particularly interest us: i) how to enhance interface design using cognitive measurement, and ii) how to improve the measurement of user efficiency

We are interested in contributing to the theoretical foundation for HCI by discovering and introducing more techniques for interface design and improvement, as well as for measurement and preprocessing of implicit inputs. Broadly, they fall under the following three themes:

Broadly, they fall under the following three themes:

Enable Users to Define their Own Visual Concepts: This can be achieved by evaluating the current models while providing new and existing interaction styles and evaluate the performance when adding and removing each one of those. This evaluation will help to determine the best model and create novel interfaces with optimal interaction styles.

Convey Measurement Confidence for Massive Data: We investigate machine learning techniques and theoretical analysis to explore and understand massive datasets in this context.

Explore the Potential of HCI in Industry: We study how to adopt interfaces based on user preference styles, especially in hand-held or mo-bile applications with no information from implicit data.

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