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
  • 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
  • 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

  • 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 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 290: Intermediate Topics (GE)
  • BIOSTAT 707: Statistical Methods for Learning and Discovery

In the News

Representative Publications

  • 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.
  • Jeong, Hayoung, Ali R. Roghanizad, Hiral Master, Juseong Kim, Aymone Kouame, Paul A. Harris, Melissa Basford, Kayla Marginean, and Jessilyn Dunn. “Data from the All of Us research program reinforces existence of activity inequality.” NPJ Digital Medicine 8, no. 1 (January 2025): 8. https://doi.org/10.1038/s41746-024-01358-4.
  • Akre, Samir, Darsol Seok, Christopher Douglas, Adrian Aguilera, Simona Carini, Jessilyn Dunn, Matthew Hotopf, David C. Mohr, Alex A. T. Bui, and Nelson B. Freimer. “Advancing digital sensing in mental health research.” NPJ Digital Medicine 7, no. 1 (December 2024): 362. https://doi.org/10.1038/s41746-024-01343-x.
  • Wang, Will Ke, Hayoung Jeong, Leeor Hershkovich, Peter Cho, Karnika Singh, Lauren Lederer, Ali R. Roghanizad, et al. “Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.” JAMIA Open 7, no. 4 (December 2024): ooae111. https://doi.org/10.1093/jamiaopen/ooae111.
  • Cunningham, Jonathan W., William T. Abraham, Ankeet S. Bhatt, Jessilyn Dunn, G Michael Felker, Sneha S. Jain, Christopher J. Lindsell, et al. “Artificial Intelligence in Cardiovascular Clinical Trials.” J Am Coll Cardiol 84, no. 20 (November 12, 2024): 2051–62. https://doi.org/10.1016/j.jacc.2024.08.069.