Jessilyn Dunn

Assistant Professor of Biomedical Engineering

Developing new AI and sensing 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 Medicine
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member in the Duke Clinical Research Institute

Contact Information

  • Office Location: 534 Research Dr, Durham, NC 27708
  • Websites:

Education

  • Ph.D. Georgia Institute of Technology, 2015

Research Interests

Generation and use of large-scale biomedical datasets for AI modeling and digital biomarker development to guide personalized, just-in-time therapies.

Courses Taught

  • ISS 796T: Bass Connections Information, Society & Culture Research Team
  • ISS 795T: Bass Connections Information, Society & Culture Research Team
  • ISS 396T: Bass Connections Information, Society & Culture Research Team
  • ISS 395T: Bass Connections Information, Society & Culture Research Team
  • ISS 290S: Special Topics in Information Science + Studies
  • EGR 393: Research Projects in Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • BME 792: Continuation of Graduate Independent Study
  • BME 791: Graduate Independent Study
  • BME 790L: Advanced Topics with the Lab for Graduate Students in Biomedical Engineering
  • BME 789: Internship in Biomedical Engineering
  • BME 590: Special Topics in Biomedical Engineering
  • BME 580: An Introduction to Biomedical Data Science (GE)
  • BME 494: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 390L: Special Topics with a Lab
  • BME 290: Intermediate Topics (GE)
  • BIOSTAT 707: Statistical Methods for Learning and Discovery

In the News

Representative Publications

  • Parthasarathy, Sairam, Shari Brosnahan, Solveig Sieberts, Elias Neto, Yanling Li, Meghasyam Tummalacherla, Heather-Elizabeth Brown, et al. “Wearable-derived Sleep Measurements are Associated with Long-COVID in the RECOVER Adult Cohort.” Res Sq, September 3, 2025. https://doi.org/10.21203/rs.3.rs-7422764/v1.
  • Jain, Sneha S., Bobak J. Mortazavi, Seng Chan You, Xiaoxi Yao, Carolyn S. P. Lam, Pierre Elias, Timothy J. Poterucha, et al. “Moving Beyond the Model: Our Perspective on Meaningful AI Research in Cardiovascular Care.” Journal of the American College of Cardiology 86, no. 10 (September 2025): 691–95. https://doi.org/10.1016/j.jacc.2025.07.040.
  • Chahal, C Anwar A., Fares Alahdab, Babken Asatryan, Daniel Addison, Nay Aung, Mina K. Chung, Spiros Denaxas, et al. “Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine.” Circulation. Genomic and Precision Medicine 18, no. 3 (June 2025): e004624. https://doi.org/10.1161/circgen.124.004624.
  • Taguibao, Candice, Salma Ajraoui, Jake Centra, Kieran F. Reid, Christina Daskalopoulou, Alberto Conde Freniche, Alan L. Hamilton, et al. “Identifying Concepts of Physical Activity Which Are Clinically Meaningful to Patients and Care Providers: A Systematic Review of Qualitative Research.” Clinical and Translational Science 18, no. 4 (April 2025): e70191. https://doi.org/10.1111/cts.70191.
  • Cho, Peter J., Iredia M. Olaye, Md Mobashir Hasan Shandhi, Eric J. Daza, Luca Foschini, and Jessilyn P. Dunn. “Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.” The Lancet. Digital Health 7, no. 1 (January 2025): e23–34. https://doi.org/10.1016/s2589-7500(24)00219-x.