Jessilyn Dunn

Assistant Professor in the Department of Biomedical Engineering

Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.

Appointments and Affiliations

• Assistant Professor of Biomedical Engineering
• Assistant Professor of Biostatistics & Bioinformatics
• Assistant Professor in the Department of Electrical and Computer Engineering
• Member in the Duke Clinical Research Institute

Contact Information

• Office Location: 534 Research Dr, Room #448, Durham, NC 27708
• Email Address: jessilyn.dunn@duke.edu
• Websites:
  • Dunn Lab

Education

• Ph.D. Georgia Institute of Technology, 2015

Research Interests

Use of large-scale biomedical datasets to model and guide personalized therapies.

Courses Taught

• BIOSTAT 707: Statistical Methods for Learning and Discovery
• BME 290: Intermediate Topics (GE)
• BME 493: Projects in Biomedical Engineering (GE)
• BME 494: Projects in Biomedical Engineering (GE)
• BME 580: An Introduction to Biomedical Data Science (GE)
• BME 590: Special Topics in Biomedical Engineering
• BME 791: Graduate Independent Study
• BME 792: Continuation of Graduate Independent Study
• BME 899: Special Readings in Biomedical Engineering
• EGR 393: Research Projects in Engineering
• HLTHPOL 395: Bass Connections COVID-19 Research Team
• HLTHPOL 395T: Health Policy & Innovation Research Team
• HLTHPOL 396T: Bass Connections Health Policy & Innovation Research Team
• HLTHPOL 795: Bass Connections COVID-19 Research Team
• HLTHPOL 795T: Bass Connections Health Policy & Innovation Research Team
• HLTHPOL 796T: Bass Connections Health Policy & Innovation Research Team
• ISS 290S: Special Topics in Information Science + Studies
• ISS 395T: Bass Connections Information, Society & Culture Research Team
• ISS 396T: Bass Connections Information, Society & Culture Research Team
• ISS 795T: Bass Connections Information, Society & Culture Research Team
• ISS 796T: Bass Connections Information, Society & Culture Research Team

In the News

• How You Can Help Scientists Better Understand COVID Variants With Wearable Devices (Jan 27, 2022 | Duke MEDx)
• Duke Celebrates Women and Girls in Science Day (Feb 10, 2021)
• Early Detection of COVID-19: How Your Smartwatch Could Help (Aug 25, 2020 | Duke Magnify)
• School of Medicine Forum Addresses the Role of Data Science During Times of Crisis (Jul 22, 2020 | School of Medicine)
• A COVID-19 Study for Early Detection Expands to Reach New Communities (Jun 15, 2020 | Pratt School of Engineering)
• Here'e How to Make Smartwatch Health Data Useful for Research (May 15, 2020)
• Using Smartphones in the Effort for Early Detection of COVID-19 (Apr 8, 2020 | Pratt School of Engineering)
• NC Survey Tracks How Residents Are Changing Behavior In Pandemic (Apr 6, 2020)
• Your Skin Tone Won't Affect Your Heart-Tracking Device. Your Activity Might (Feb 11, 2020 | Pratt School of Engineering)
• Jessilyn Dunn: Gaining Insights from Biomedical Big Data (Jun 5, 2018 | Duke University Pratt School of Engineering)

Representative Publications

• Chikwetu, L; Miao, Y; Woldetensae, MK; Bell, D; Goldenholz, DM; Dunn, J, Does deidentification of data from wearable devices give us a false sense of security? A systematic review., The Lancet Digital Health, vol 5 no. 4 (2023), pp. e239-e247 [10.1016/s2589-7500(22)00234-5] [abs].
• Popham, S; Burq, M; Rainaldi, EE; Shin, S; Dunn, J; Kapur, R, An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study, Jmir Biomedical Engineering, vol 8 (2023), pp. e43726-e43726 [10.2196/43726] [abs].
• Hughes, A; Shandhi, MMH; Master, H; Dunn, J; Brittain, E, Wearable Devices in Cardiovascular Medicine., Circulation Research, vol 132 no. 5 (2023), pp. 652-670 [10.1161/circresaha.122.322389] [abs].
• Holko, M; Lunt, C; Dunn, J, Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access., Pacific Symposium on Biocomputing, vol 28 (2023), pp. 1-6 [abs].
• Lederer, L; Breton, A; Jeong, H; Master, H; Roghanizad, AR; Dunn, J, Considerations while using Fitbit Data in the All of Us Research Program: Tutorial (Preprint) (2022) [10.2196/preprints.45103] [abs].