Cognitive & Brain Science researcher at Tufts University focused on how people interact with autonomous systems. NSF REU participant and UR2PhD Scholar applying human factors methods to human-robot interaction and explainable AI.
Featured research
Human Interaction with Robotic Control Systems
Human FactorsDeveloped simulation environments to study how users form mental models of autonomous robotic systems. Conducted structured human-subjects studies identifying misalignments between user expectations and system behavior, translating empirical findings into human factors design recommendations.
Move the basketball with arrow keys. The system infers your goal in real time and blends its suggestion with your input.
Use arrow keys to move. The arrow shows the robot's suggested direction. Try switching arbitration modes.
Generative AI in K–12 Learning Platforms
Explainable AILed product vision for integrating generative AI into a Blockly-based coding platform serving K–12 learners. Conducted usability evaluations to surface accessibility gaps and built an interactive prototype to validate design decisions around learner comprehension and AI transparency.
A Blockly-based K–12 coding environment built as part of the GenAI integration project. Drag blocks from the toolbox and press Run to animate the sprite.
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