Sarah Jiang

Hi! I'm a Biomedical Engineering PhD student at Duke University, where I work on developing digital health tools and deep learning methods to improve health outcomes and healthcare access in the BIG IDEAs Lab, advised by Dr. Jessilyn Dunn. I'm broadly interested in leveraging wearable devices and biosignals for remote monitoring and early detection and intervention of chronic illness. I'm fortunate to be funded by the NSF Graduate Research Fellowship.

Previously, I completed my BSE in Biomedical Engineering and Computer Science at Duke University, where I conducted research on wearable sensor advancements and biosignal data demographics, and developed full-stack applications for healthcare data management. I also interned at UCLA where I was advised by Dr. Yuzhe Yang. At UCLA, I evaluated various self-supervised learning objectives to build a general purpose motion foudation model and created a unified dataset of high resolution accelerometer data for activity and disease tasks.

When I'm not working on a project, you can find me playing the piano, exploring local coffee shops, or buried in a good book [currently reading: Katabasis by RF Kuang].

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Research

Interested in AI for multimodal biomedical data, with a focus on wearable sensing and clinical applications.

Comprehensive analysis of demographic reporting and biases in wearable biosignal datasets and their implications for ML model clinical deployment.

By identifying statistically significant determinants that influence relapse, we seek to inform the development of digital health technologies for supporting relapse prevention efforts. Full paper under review.

Systematic review of SOTA wearable sensor technologies for cardiovascular health monitoring and functional capacity assessment against gold-standard clinical measures.

Modified code from Jon Barron.