I study privacy issues arising from the interplay of technology, public policy, and user interaction. My current projects focus on consumer Internet of Things (IoT) devices, including "smart" home appliances, health monitors, and children's toys. I employ a variety of data-driven research techniques, from network traffic analysis and machine learning to human-computer interaction methods. This interdisciplinary approach provides a broad view of IoT privacy concerns and helps generate recommendations for device manufacturers, regulators, and consumer advocates.
I am also interested in applying machine learning to noisy biological data. I have used convolutional networks to improve automated segmentation of calcium and electron microscopy images for neuroscience research. I have also worked on inferring blood glucose concentration from non-invasive mid-infrared laser spectrometry data.
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