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Machine Learning

Computer Vision

CHI 2018

Featured Projects

Static visualizations have analytic and expressive value. However, many interactive tasks cannot be completed using static visualizations. As datasets grow in size and complexity, static visualizations start losing their analytic and expressive power for interactive data exploration. Despite this limitation of static visualizations, there are still many cases where visualizations are limited to being static (e.g., visualizations on presentation slides or posters). We believe in many of these cases, static visualizations will benefit from allowing users to perform interactive tasks on them.
Using the Z-wave protocol stack, we are building a controller for the Aware Home using a Raspberry Pi that will allow users to control and query device data on a dashboard. This collected data will then be used to predict usage patterns and serve tips for power saving. Finally, a user-friendly rules engine enables users to create certain rules using sensor data.
About 1 in 7 U.S. children will be affected with a developmental disability including Autism and Attention Deficit Hyperactivity Disorder (ADHD). Early diagnosis of developmental delays ensures proper intervention and overall improved qualities of life. In this research, we investigate how parents of young children use an Android mobile application, ActEarly, to log their children's developmental progress through milestones tracking techniques.

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