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
  • Websites:

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 795T: Bass Connections Information, Society & Culture Research Team

In the News

Representative Publications

  • Shandhi, Md Mobashir Hasan, Peter J. Cho, Ali R. Roghanizad, Karnika Singh, Will Wang, Oana M. Enache, Amanda Stern, et al. “A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.” Npj Digit Med 5, no. 1 (September 1, 2022): 130. https://doi.org/10.1038/s41746-022-00672-z.
  • Jiang, Y., W. Wang, T. Scargill, M. Rothman, J. Dunn, and M. Gorlatova. “Digital biomarkers reflect stress reduction after Augmented Reality guided meditation: A feasibility study.” In Digibiom 2022  Proceedings of the 2022 Emerging Devices for Digital Biomarkers, 29–34, 2022. https://doi.org/10.1145/3539494.3542754.
  • Erickson, Melissa L., Will Wang, Julie Counts, Leanne M. Redman, Daniel Parker, Janet L. Huebner, Jessilyn Dunn, and William E. Kraus. “Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology.” J Biol Rhythms 37, no. 3 (June 2022): 260–71. https://doi.org/10.1177/07487304221087068.
  • Goergen, Craig J., MacKenzie J. Tweardy, Steven R. Steinhubl, Stephan W. Wegerich, Karnika Singh, Rebecca J. Mieloszyk, and Jessilyn Dunn. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Vol. 24, 2022. https://doi.org/10.1146/annurev-bioeng-103020-040136.
  • Cho, Peter Jaeho, Jaehan Yi, Ethan Ho, Md Mobashir Hasan Shandhi, Yen Dinh, Aneesh Patil, Leatrice Martin, et al. “Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design.” Jmir Mhealth Uhealth 10, no. 4 (April 8, 2022): e29510. https://doi.org/10.2196/29510.