Jessilyn Dunn

Assistant Professor 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
• 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

• 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].
• Shandhi, MMH; Dunn, JP, AI in medicine: Where are we now and where are we going?, Cell Rep Med, vol 3 no. 12 (2022) [10.1016/j.xcrm.2022.100861] [abs].
• Wang, WK; Chen, I; Hershkovich, L; Yang, J; Shetty, A; Singh, G; Jiang, Y; Kotla, A; Shang, JZ; Yerrabelli, R; Roghanizad, AR; Shandhi, MMH; Dunn, J, A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications., Sensors (Basel, Switzerland), vol 22 no. 20 (2022) [10.3390/s22208016] [abs].
• Shandhi, MMH; Cho, PJ; Roghanizad, AR; Singh, K; Wang, W; Enache, OM; Stern, A; Sbahi, R; Tatar, B; Fiscus, S; Khoo, QX; Kuo, Y; Lu, X; Hsieh, J; Kalodzitsa, A; Bahmani, A; Alavi, A; Ray, U; Snyder, MP; Ginsburg, GS; Pasquale, DK; Woods, CW; Shaw, RJ; Dunn, JP, A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19., Npj Digital Medicine, vol 5 no. 1 (2022) [10.1038/s41746-022-00672-z] [abs].
• Jiang, Y; Wang, W; Scargill, T; Rothman, M; Dunn, J; Gorlatova, M, Digital biomarkers reflect stress reduction after Augmented Reality guided meditation: A feasibility study, Digibiom 2022 Proceedings of the 2022 Emerging Devices for Digital Biomarkers (2022), pp. 29-34 [10.1145/3539494.3542754] [abs].