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Data Annotation, Model Debugging, and Data Work / Contextual Example Selection and Rare Pattern Coverage

Large amounts of unlabeled data on wearable devices are difficult to leverage for accurate activity recognition.

lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage

Large amounts of unlabeled data on wearable devices are difficult to leverage for accurate activity recognition.

UbiComp '22Assessing the State of Self-Supervised Human Activity Recognition Using Wearables

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lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage

Children lack suitable AI learning tools to effectively master AI fundamentals.

CHI '25Technologies for Children’s AI Learning: Design Features and Future Opportunities

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Students in online education struggle to actively ask questions and explore knowledge from video-based learning.

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Users often overgeneralize complex data due to confirmation bias, leading to suboptimal decisions.

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Data slicing and model optimization depend on metadata, making operations complex and costly.

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WiFi activity recognition struggles to adapt across environments and individuals and requires large amounts of labeled data.

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Users frequently decline proactive learning tasks from smart speakers due to poor timing.

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