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This project asks: can we use visual design to let people navigate information at their own depth? The core idea is progressive disclosure through visual cues. Specifically, using symbols, icons, and glyphs to signal that more detail exists, and revealing that detail only when someone expresses interest (by clicking, hovering, or zooming in). Think of it like a map: at a distance, you see city names; as you zoom in, streets appear; closer still, individual buildings. We want to apply that same logic to arbitrary information.

Tags: Basic Programming, Human Computer Interaction (HCI), Visualization, 3rd Year+

Attention-deficit/hyperactivity disorder (ADHD) affects an estimated 5–10% of children worldwide. Yet existing interventions — medication and clinic-based therapy — remain costly and difficult to access for many families. Neurofeedback training is a non-pharmacological approach with a growing evidence base, but it is currently available almost exclusively in clinical settings.

Our research asks: What should an at-home attention training system look like for families of children with ADHD? We are designing a system that combines an EEG headset, tangible interactive hardware, and gamified training experiences — one that children actually want to use, that parents can meaningfully participate in, and that makes training progress visible and trackable.

Tags: Basic Programming, Figma, Human Computer Interaction (HCI), Psychology, 2nd Year +

Healthcare data can reveal important insights that improve patient care, but analyzing it is challenging. Analysts must explore complex datasets, generate and test hypotheses, and interpret results carefully. While Generative AI can assist by creating code, visualizations, and insights, it does not always understand users' goals and can sometimes produce unreliable results. This project explores how teams of AI agents can collaborate with humans to support healthcare data analysis. We will design new interaction techniques that help people communicate their intent, understand how AI-generated results were produced, and assess whether those results are trustworthy. By making human-AI collaboration more transparent and reliable, this research aims to help healthcare professionals gain insights from data more effectively and make better-informed decisions.

Tags: Web Development, Data Analysis, Human Computer Interaction (HCI), Artificial Intelligence, All Years

This project aims to enhance a research platform for creating and analyzing interactive, web-based data visualization studies by adding an eye-tracking analysis toolkit. Eye-tracking can help researchers understand where users focus, how they analyze problems, and how they make decisions while interacting with websites and data visualizations. However, analyzing gaze data often requires expensive commercial software. This project aims to address that challenge by developing an open and accessible toolkit for analyzing common gaze measures from recorded user studies. By simplifying gaze analysis, the toolkit could support the development of adaptive visualization systems that respond to users’ needs and difficulties.

Tags: Human Computer Interaction (HCI), Python, React, All Years