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
  • HLTHPOL 796T: Bass Connections Health Policy & Innovation Research Team
  • HLTHPOL 795T: Bass Connections Health Policy & Innovation Research Team
  • HLTHPOL 396T: Bass Connections Health Policy & Innovation Research Team
  • HLTHPOL 395T: Health Policy & Innovation Research Team
  • EGR 393: Research Projects in Engineering
  • BME 899: Special Readings in Biomedical 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

  • 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.
  • Shin, Sooyoon, Nathan Kowahl, Taylor Hansen, Albee Y. Ling, Poulami Barman, Nicholas Cauwenberghs, Erin Rainaldi, et al. “Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study.” J Card Fail 30, no. 11 (November 2024): 1423–33. https://doi.org/10.1016/j.cardfail.2024.02.028.
  • Jiang, Sarah, Perisa Ashar, Md Mobashir Hasan Shandhi, and Jessilyn Dunn. “Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database.” The Lancet. Digital Health 6, no. 11 (November 2024): e871–78. https://doi.org/10.1016/s2589-7500(24)00170-5.
  • Armoundas, Antonis A., Faraz S. Ahmad, Derrick A. Bennett, Mina K. Chung, Leslie L. Davis, Jessilyn Dunn, Sanjiv M. Narayan, et al. “Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.” Circulation. Genomic and Precision Medicine 17, no. 3 (June 2024): e000095. https://doi.org/10.1161/hcg.0000000000000095.